remote sensing

Article Radar Measurements of Morphological Parameters and Species Identification Analysis of Migratory

Cheng Hu 1,2, Shaoyang Kong 1 , Rui Wang 1,* and Fan Zhang 1 1 Radar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, 2 Key Laboratory of Electronic and Information Technology in Satellite Navigation (Beijing Institute of Technology), Ministry of Education, Beijing 100081, China * Correspondence: [email protected]; Tel.: +86-1340-109-9936

 Received: 19 July 2019; Accepted: 19 August 2019; Published: 22 August 2019 

Abstract: Migratory identification has been concerning entomology and pest managers for a long time. Their nocturnal behavior, as well as very small radar cross-section (RCS), makes individual detection challenging for any radar network. Typical entomological radars work at the X-band (9.4 GHz) with a vertical pencil beam. The measured RCS can be used to estimate insect mass and wingbeat frequency, and then migratory insects can be categorized into broad taxon classes using the estimated parameters. However, current entomological radars cannot achieve species identification with any higher precision or confidence. The limited frequency range of current insect radars have precluded the acquisition of more information useful for the identification of individual insects. In this paper, we report an improved measurement method of insect mass and body length using a radar with many more measurement frequencies than current entomological radars. The insect mass and body length can be extracted from the multi-frequency RCSs with uncertainties of 16.31% and 10.74%, respectively. The estimation of the thorax width and aspect ratio can also be achieved with uncertainties of 13.37% and 7.99%, respectively. Furthermore, by analyzing the statistical data of 5532 insects representing 23 species in East China, we found that the correct identification probabilities exceed 0.5 for all of the 23 species and are higher than 0.8 for 15 of the 23 species under the achievable measurement precision of the proposed technique. These findings provide promising improvements of individual parameter measurement for entomological radars and imply a possibility of species identification with higher precision.

Keywords: insects; multi frequency; radar cross-sections; parameter extraction; support vector machines

1. Introduction migration is increasingly recognized to have ecological effects on both habitats and ecosystems by transport effects and trophic effects [1–3]. These ecological interactions make every species of migrant play an irreplaceable role in the entire circulatory system [4]. Among animal migrants, the migratory insects have the largest individual numbers [5]. Many migratory insects provide essential ecosystem services such as crop pollination, seed spreading and energy transfer [4], but some migratory pests cause losses to human society, such as crop damages [2,6] and virus transmission [7]. Therefore, studies on insect migration are critically important. Most migratory insects are too small (a few milligrams of individual mass) for individual tracking, and their high flying altitudes (a few hundred meters, or even 2–3 km) during migration also limit their effective monitoring [8]. Therefore, the knowledge of insect migration has lagged behind that of

Remote Sens. 2019, 11, 1977; doi:10.3390/rs11171977 www.mdpi.com/journal/remotesensing Remote Sens. 2019, 11, 1977 2 of 19 other aerial organisms such as birds and bats. To study insect migration, many tools and techniques, including aerial net sampling and searchlight trapping have been applied [9,10]. In contrast to these traditional monitoring tools and techniques, radars have the advantages of a long detection range and the capability for continuous observation [1,11]. The distinctive capacity of radar to monitor over-flying nocturnal insects makes it widely used in the study of the migration behavior of high-altitude migratory insects. Researchers in entomology have built various kinds of entomological radars, including airborne entomological radar [12,13], scanning entomological radar [14], tracking entomological radar [15] and vertical-looking radar (VLR) [16–18]. Among these radars, scanning entomological radar and vertical entomological radar are the most widely used in the research of insect migration, which have greatly promoted the development of radar entomology. Since the late 2000s, radar-led research has refined our knowledge of the strategies and sensory capabilities employed by migratory insects in flight at night such as navigation, aggregation and orientation [19–21]. With the development of insect migration research, individual observation is becoming increasingly important. To study the behavior of individual insects, the first and the second generations of VLRs were built [22,23]. VLRs not only enable the long-term automated monitoring of migratory insects, but also have the ability to retrieve the mass, wing frequency and shape-related parameters of the target insect from the radar echo [24], which can be used to identify high-flying insect species [25]. The identification of insect migrants is an essential step in understanding the role of specific insects in population dynamics and community interactions, which may contribute to a better understanding of the ecological significance of insect migration. However, the ability of existing entomological radars to identify species identification is still insufficient. The detected insects can only be assigned to broad taxonomic classes (e.g., big or small moth) in most cases [26]. It is therefore necessary to improve the capability of the current entomological radar in species identification. The morphological parameters of insects (e.g., shape and size) are the main basis of species identification [27]. To more precisely identify insect species, species-exclusive parameters should be estimated from radar data, and the estimation accuracy of these parameters must be improved. The wingbeat frequency, mass, body length, aspect ratio (i.e., the ratio of length to width) and thorax width are the five most important morphological parameters. The wingbeat frequency of a flying insect can be measured by a VLR with an uncertainty of 1 Hz [28]. For insect mass and body length, the multi-frequency radar cross-sections (RCSs) were investigated and the estimation of mass and body length could be achieved with uncertainties of 23.7% and 15.7%, respectively [29]. Aside from species identification, the estimation of biomass of high-flying insects can contribute to the study of the relationship between migration of insects and its impacts on the ecosystems, and it can be profoundly affected by the estimation of insect mass [2], with both requiring a higher estimation accuracy of insect mass. In addition, a significant relationship has been found between the aspect ratio and RCS shape (i.e., the ratio of the RCSs for the polarization direction parallel and perpendicular to the body axis), which is only limited to small insects [26]. Among the research of morphological parameter measurement with insect radar, little has been done to investigate the relationship between insect thorax width and radar data. In our experiment, the multi-frequency RCSs of 15 insects (belonging to 11 species and 3 families, caught in Jiangsu, East China) were measured using an experimental multi-frequency radar. The frequency dependence of insect RCS was fully studied, and the estimation accuracy of mass and body length was further improved. The measured RCS vs. frequency curves were proven to contain information highly correlated with the aspect ratio and thorax width, and therefore using multi-frequency RCSs may be useful for improving the aspect ratio and thorax width estimation. Under the assumption that the mass, body length, wingbeat frequency, aspect ratio and thorax width of flying insects can be measured by insect radar using the proposed technique, an identification simulation was implemented by analyzing the statistical records of insect migrants (belong to 23 species and 7 families, trapped in the Bohai migration corridor, China). The results indicate that the correct identification probabilities exceed 0.5 for all of the 23 species and are higher than 0.8 for 15 of the Remote Sens. 2019, 11, 1977 3 of 19

23 species. The identification simulation demonstrated that accurate estimations of the morphological parameters of migratory insects could increase the identification capability of entomological radar. The remainder of this paper is organized as follows. In Section2, the multi-frequency RCS measurements of migratory insects are first introduced and the estimation accuracy of the morphological parameters with experimental data is then analyzed. In Section3, we provide an evaluation of the identification performance on the migratory insects based on their morphological parameters. Section4 outlines the discussion concerning the strengths and weaknesses of the proposed method as well as its future improvement and application. Our main conclusion is outlined in Section5.

2. Estimation of Morphological Parameters RCS represents an inherent property (i.e., scattering ability) of a target and defines how a target intercepts and redirects an electromagnetic wave [30]. The measured RCS is a function of shape, composition and aspect of the target and the wavelength and polarization of the transmitted radiation [30,31]. For the radar scattering characteristics of insects, the polarization, temporal and angular variations of the RCSs are the main research foci [26,32,33]. Variations in RCS across the frequency range have been proven to carry some information that can be used to estimate the insect morphological parameters [29]. However, the morphological parameters that can be measured using radar are limited, and current inversion methods of insect mass and body length have insufficient accuracy [26,29]. Therefore, further research was conducted to extract other potential parameters and improve the estimation accuracy.

2.1. Multi-Frequency Insect RCS Measurements The multi-frequency RCSs of migratory insects are required for research on the frequency dependence of insect RCSs. For this, an experimental multi-frequency radar was constructed with a vector network analyzer and four horn antennas (two antennas working at X-band and the other two antennas working at K-band) in a microwave anechoic chamber [29]. A total of 15 insects were trapped by a light trap the night before the experiment in our research base located in Soochow, China. Each insect adhered to a short polyethylene (PE) thread on its back, and then was hung directly above the antennas with its body axis direction parallel to the polarization direction of the antennas. The variation of the RCS (i.e., σ) with the direction of polarization angle φ can be represented as [31] σ(φ) = a [1 + α cos 2(φ β) + α cos 4(φ β)], (1) 0 2 − 4 − where α0 is the polarization-averaged RCS; α2 and α4 are dimensionless parameters with non-negative values [31,34]; and β represents the insect’s body axis (i.e., the orientation of the insect). These three coefficients (i.e., a0, α2 and α4) can be used to calculate the two conventional RCS values of the target (σxx and σyy) as follows [26]:

σxx = a0(1 + α2 + α4), (2)

σyy = a (1 α + α ). (3) 0 − 2 4 When the longitudinal axis of the insect’s body is parallel to the polarization plane, the RCS value is usually the maximum of the polarization pattern, denoted as σxx. σyy is the RCS value when the polarisation plane is orthogonal to that of σxx [33,35]. Considering the selection of the polarization direction in our experiment, the measured RCS of the insects was also denoted as σxx to be consistent with the former research [31,33]. The measurement results can be found in Tables S1 and S2. Each insect was alive during the experiment. Before the experiment, the mass, body length and thorax width of each insect were measured and recorded, and the aspect ratio was calculated as shown in Table S3. The mass, body length, thorax width and aspect Remote Sens. 2019, 11, 1977 4 of 19 Remote Sens. 2019, 11, x FOR PEER REVIEW 4 of 19

For convenience, each insect was identified with a distinguished class label following an uppercase ratio ranged from 11.5 to 514.5 mg, 6 to 28 mm, 1 to 5 mm and 5.0 to 8.4, respectively. For convenience, letter order. each insect was identified with a distinguished class label following an uppercase letter order.

2.2.2.2. Multi-Frequency Multi-Frequency Scattering Scattering Characteristic Characteristic Analysis Analysis HobbsHobbs et al.et al. presented presented the the RCS RCS measurements measurements for for 68 68 insects, insects, representing representing 24 24 species species with with an an 𝜎 insectinsect mass mass from from 9 mg 9 mg to 3 to g 3 at g a at frequency a frequency of 9.4 of GHz9.4 GHz [33]. [33]. The The measured measuredσxx–mass –mass curve curve is shown is shown in Figurein Figure1. The 1. curve The cancurve be dividedcan be divided into three into sections: three sections: Rayleigh Rayleigh region, resonance region, resonance region and region optical and region.optical In theregion. first In section, the first when section, the scattering when the of scatte smallring insects of small (mass insects< 100 (ma mg)ss at < X-band 100 mg) is at generally X-band is 𝜎 in thegenerally Rayleigh in region,the Rayleighσxx is positivelyregion, related is positively to mass related [33]. As to the mass insect [33]. mass As increases,the insect themass scattering increases, of insectsthe scattering falls into of insects the resonance falls into region the resonance where the region RCS varieswhere inthe a RCS more varies complicated in a more way complicated due to way due to the size to wavelength ratio being close to unity. Therefore, the 𝜎 and mass are the size to wavelength ratio being close to unity. Therefore, the σxx and mass are negatively related. Whennegatively the insect related. is quite When large the (mass insect> 1000 is quite mg), large the scattering(mass > 1000 begins mg), to the fall scattering into the optical begins region to fall ininto the optical region in which the target size is significantly larger than the wavelength. In the optical which the target size is significantly larger than the wavelength. In the optical region, σxx regains its positiveregion, correlation 𝜎 regains with its mass. positive correlation with mass.

Rayleigh Resonance Optical region region region

Figure 1. σxx versus mass at a frequency of 9.4 GHz. Note: Red squares denote radar cross-section

(RCS)Figure data 1. provided 𝜎 versus in mass the study at a frequency of Hobbs of et 9.4 al. [GHz.33] and Note: black Red lines squares denote denote approximate radar cross-section fitting.

σxx(RCS)represents data theprovided RCS value in the when study the of bodyHobbs axis et al direction. [33] and is parallelblack lines to thedenote polarization approximate direction. fitting. 𝜎 represents the RCS value when the body axis direction is parallel to the polarization direction. RCS is related to insect mass [27]. To explore the variation of this relationship across the frequency range, theRCSσxx isvs. related mass curvesto insect at dimassfferent [27]. frequencies To explore obtained the variation from our of measurements this relationship are shown across in the

Figurefrequency2. Through range, contrastive the 𝜎 vs. analysis, mass curves we found at different that the frequeσxx vs.ncies mass obtained curves from at other our measurements frequencies haveare similar shown shapes in Figure as those2. Through at a frequency contrastive of 9.4 analysis, GHz. Thewe found data presented that the 𝜎 in Figure vs. mass2a illustrate curves at the other behaviorfrequencies of the have insects’ similar RCS shapes at the endas those of the at Rayleigha frequency region of 9.4 towards GHz. The the firstdata RCSpresented maxima in forFigure the 2a X-bandillustrate frequency. the behavior As expected, of the theinsects’ RCS increasesRCS at the with end the of increasethe Rayleigh in frequency, region towards and then the reaches first RCS a maximum.maxima for The the maximum X-band frequency. point moves As in expected, negative the directions RCS increases for both with the horizontalthe increase and in verticalfrequency, axes and as thethen frequency reaches a increases.maximum. The The data maximum presented point in Figuremoves2 inb illustratenegative directions the behavior for inboth the the resonance horizontal regionand towardvertical theaxes optical as the regionfrequency for theincreases. K band The frequency. data presented These phenomenain Figure 2b can illustrate be explained the behavior by thein sizes the ofresonance these insects region becoming toward the comparable optical region to the for wavelength the K band for frequency. the K-band These frequency, phenomena leading can to thebe explained scattering by of thesethe sizes insects of these in the insects resonance becomi regionng comparable toward the to optical the wavelength region. For for the the insects K-band withfrequency, a mass larger leading than to 100the scattering mg, the RCSs of these at K-band insects are in the smaller resonance than thoseregion at toward X-band, the which optical can region. be explainedFor the byinsects RCS with oscillating a mass in larger the resonant than 100 region. mg, the In RCSs addition, at K-band a high frequency,are smaller such than as those K-band, at X-band, has thewhich advantage can be of explained a larger RCS by RCS when oscillating detecting in small the reso insects,nant which region. can In provideaddition, recommendations a high frequency, for such radaras K-band, system designs. has the advantage of a larger RCS when detecting small insects, which can provide recommendations for radar system designs.

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(a) (b)

Figure 2. σxx versus mass at different frequencies: (a) X-band; (b) K-band. Note: Discrete points denote

measuredFigure 2. radar 𝜎 cross-sectionversus mass at in ourdifferent experiment. frequencies: For better (a) X-band; observation, (b) K-band. lines of Note: the same Discrete colors points denote simpledenote polynomial measured fittingsradar cross-sect to them.ion The in fit our order experiment. is 3 for data For atbetter X-band observation, and 5 for datalines atof K-band.the same colors denote simple polynomial fittings to them. The fit order is 3 for data at X-band and 5 for data 2.3. Multi-Frequencyat K-band. Scattering Feature Extraction

The previous subsection explored how the σxx vs. mass curve varies with frequency. To further 2.3. Multi-Frequency Scattering Feature Extraction investigate the frequency dependence of insect RCS, we explored the relationship between the σxx vs. frequencyThe curvesprevious and subsection the morphological explored how parameters the 𝜎 vs. of insects.mass curve Firstly, varies a simulation with frequency. was conductedTo further to comprehensivelyinvestigate the frequency analyze thedependence multi-frequency of insect RCSRCS, characteristics we explored the due relationship to the limited between frequencies the 𝜎 of ourvs. experimental frequency curves data. Asand the the shape morphological and composition parame ofters insects of areinsects. complex, Firstly, a simple a simulation and acceptable was digitalconducted model to should comprehensively be selected analyze to emulate the multi-fr the scatteringequency characteristicsRCS characteristics of the due migratory to the limited insects. Afrequencies prolate spheroid of our experimental is widely used data. to As emulate the shape the and body composition shape of of all insects insect are taxa complex, [29,36]. a simple Another importantand acceptable factor todigital consider model is theshould dielectric be selected composition to emulate [37]. Mirkovicthe scattering demonstrated characteristics that aof prolate the ellipsoidmigratory with insects. an internal A prolate dielectric spheroid of homogenized is widely used chitin to emulate and hemolymph the body sh mixtureape of canall insect emulate taxa true [29,36]. Another important factor to consider is the dielectric composition [37]. Mirkovic insect characteristics [36]. A prolate ellipsoid with an internal dielectric of the spinal cord best replicates demonstrated that a prolate ellipsoid with an internal dielectric of homogenized chitin and the experiment measurements [29]. In this study, the spinal cord prolate spheroids were used as the hemolymph mixture can emulate true insect characteristics [36]. A prolate ellipsoid with an internal approximate model of insects for the qualitative analysis of multi-frequency scattering characteristics. dielectric of the spinal cord best replicates the experiment measurements [29]. In this study, the spinal The simulation results for different prolate spheroids are shown in Figure3. The lengths of these cord prolate spheroids were used as the approximate model of insects for the qualitative analysis of prolatemulti-frequency spheroids rangedscattering from characteristics. 5 to 28 mm, and the aspect ratio was set to 5. Least squares polynomial fitting wasThe simulation adopted of results the simulation for different data. prolate The σ spheroidsxx vs. frequency are shown curves in Figure have similar3. The lengths shapes. of The these RCS increasesprolate spheroids or oscillates ranged for each from of 5 to the 28 models mm, and as the the aspect frequency ratio was increases. set to 5. AsLeast the squares length polynomial increases, the horizontalfitting was coordinates adopted of of the the simulation global maxima data. decrease The 𝜎 andvs. frequency the vertical curves coordinates have similar of the globalshapes. maxima The increase.RCS increases The black or oscillates dots represent for each theof the estimated models as global the frequency maxima, increases. which were As the proven length useful increases, for the estimationthe horizontal of the coordinates body length of andthe global mass [29maxima]. The decrease global maximum and the ve ofrtical the RCScoordinates vs. frequency of the global curve is themaxima resonance increase. parameter, The black which dots has represent a well-documented the estimated quantity, global maxima, especially which in radar were meteorology proven useful [38 ]. Thefor estimated the estimation global of maxima the body were length also and considered mass [29]. as The characteristic global maximum information of the for RCS further vs. frequency analysis in thiscurve paper. is the resonance parameter, which has a well-documented quantity, especially in radar meteorology [38]. The estimated global maxima were also considered as characteristic information for further analysis in this paper.

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-20

-30

-40

-50

Length -60 5mm

RCS (dBsm) 6mm 7mm -70 8mm 12mm 16mm -80 20mm 24mm 28mm -90 0 5 10 15 20 25 30 35 40 Frequency (GHz)

FigureFigure 3. 3.Simulated Simulated RCS–frequency RCS–frequency curves curves for for prolate prolate spheroids. spheroids. Note: Note: BlackBlack dotsdots represent represent the the estimatedestimated global global maxima. maxima. TheThe fitfit order order is is 4 4 for for 5, 5, 6, 6, 7 7 and and 8 8 mm mm spheroids; spheroids; 6 6 for for 12, 12, 16 16 and and 20 20 mm mm spheroids;spheroids; and and 9 9 for for 24 24 and and 28 28 mm mm spheroids. spheroids. 2.4. Estimation of Morphological Parameters 2.4. Estimation of Morphological Parameters The experimental data were processed similarly to extract the estimated global maxima using least The experimental data were processed similarly to extract the estimated global maxima using squares fitting. Further details for global maxima estimation are available in the original publication [29]. least squares fitting. Further details for global maxima estimation are available in the original The global maximum of the σxx vs. frequency curve is denoted as σmax and λmax as listed in Table S3. publication [29]. The global maximum of the 𝜎 vs. frequency curve is denoted as 𝜎 and 𝜆 σmax is expressed in dBsm and λmax is expressed in mm, and these two parameters were applied to as listed in Table S3. 𝜎 is expressed in dBsm and 𝜆 is expressed in mm, and these two estimate insect morphological parameters. parameters were applied to estimate insect morphological parameters. Wang et al. demonstrate that the experimentally measured λmax and σmax are linearly related to Wang et al. demonstrate that the experimentally measured 𝜆 and 𝜎 are linearly related the body length and logarithm of mass of an insect, respectively, and the estimation of body length and to the body length and logarithm of mass of an insect, respectively, and the estimation of body length mass could be achieved with uncertainties (i.e., root mean square percentage error, see AppendixA) and mass could be achieved with uncertainties (i.e., root mean square percentage error, see Appendix of 15.7% and 23.7%, respectively [29]. However, this linear relationship was proposed based on A) of 15.7% and 23.7%, respectively [29]. However, this linear relationship was proposed based on the simulated data, and some differences were observed between the simulation models and the the simulated data, and some differences were observed between the simulation models and the experimental insects. Compared with the models, the insects have more complex shapes and the experimental insects. Compared with the models, the insects have more complex shapes and the insects are composed of different materials and different portions of the insect bodies have different insects are composed of different materials and different portions of the insect bodies have different dielectric properties. Therefore, some differences may exist between the simulation and experimental dielectric properties. Therefore, some differences may exist between the simulation and experimental data, indicating that the proposed linear relationship may be imprecise for the experimental data. data, indicating that the proposed linear relationship may be imprecise for the experimental data. Through analysis, we found that improved mass and body length estimates could be obtained by Through analysis, we found that improved mass and body length estimates could be obtained adopting a high-order polynomial fitting as shown in Figure4b,c. Insect mass estimated from σ has by adopting a high-order polynomial fitting as shown in Figure 4b,c. Insect mass estimatedmax from an uncertainty of 23.53%, and body length estimated from λmax has an uncertainty of 11.25% (Table1). 𝜎 has an uncertainty of 23.53%, and body length estimated from 𝜆 has an uncertainty of The variations of mass with λmax and body length with σmax are also shown in Figure4a,d. Based on 11.25% (Table 1). The variations of mass with 𝜆 and body length with 𝜎 are also shown in these relationships, we found that the measured λmax can also be used to extract the mass and the Figure 4a,d. Based on these relationships, we found that the measured 𝜆 can also be used to measured σmax can be used to extract the body length. All Spearman’s rank correlation coefficients extract the mass and the measured 𝜎 can be used to extract the body length. All Spearman's rank were larger than 0.85, indicating high correlations between the analyzed values. The thorax width is correlation coefficients were larger than 0.85, indicating high correlations between the analyzed also an important insect characteristic and the variations of the body with and are shown in values. The thorax width is also an important insect characteristic and theσ maxvariationsλmax of the body with Figure4e,f. Both these two parameters showed a significant relationship with the thorax width for 𝜎 and 𝜆 are shown in Figure 4e,4f. Both these two parameters showed a significant theserelationship specimens. with Therefore, the thorax the width thorax for width these can spec alsoimens. be estimated. Therefore, the thorax width can also be estimated.

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(a) (b)

(c) (d)

(e) (f)

Figure 4. Experimental and fitting results. (a,c,e) Relations between λmax and morphological parameters. Figure 4. Experimental and fitting results. (a,c,e) Relations between 𝜆 and morphological (b,d,f) Relations between σmax and morphological parameters. Note: rs is the Spearman’s rank parameters. (b,d,f) Relations between 𝜎 and morphological parameters. Note: 𝑟 is the correlationSpearman's coe rankfficient; correlationp means coefficient;p-value; and p means RMSPE p-value; represents and RMSPE root mean represents square percentageroot mean square error (see AppendixpercentageA). error (see Appendix A). Table 1. Morphological data of measured species and scattering features extracted from All estimation results were compared as listed in Table 1. The lowest estimation errors of the multi-frequency RCSs. mass and body length were 16.31% and 10.74%, reduced from 23.7% and 15.7% in Wang et al. [29], respectively. For the thoraxMass width, no paper has reported Body Length its estimation based on Thorax radar Widthmeasurement. Method 1 2 3 This is the first timers that(p ) the thoraxRMSPE width has breens (p) extracted RMSPE from multi-frequencyrs (p) RCSs RMSPE with high accuracy. The aspect ratio is also an important feature used to characterize insect shape, and this λmax 0.9526 (<0.001) 16.31% 0.8836 (<0.001) 11.25% 0.916 (<0.001) 13.37% shapeσ parametermax 0.9464 can (

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distribution of the estimated aspect ratio versus the true aspect ratio is shown in Figure 5. The aspect ratio has an estimation error of 7.99%. The best-fitting lines of insect mass, body length and thorax Remotewidth Sens. can2019 be, 11respectively, 1977 represented as 8 of 19

mass𝑚𝑔 = −771.81𝜆 + 125.54𝜆 − 7.23𝜆 + 0.17𝜆, (4) All estimation results were compared as listed in Table1. The lowest estimation errors of the mass and body length werelog 16.31%length and𝑚𝑚 10.74%, = −54.31𝜎 reduced from+ 2.94𝜎 23.7% +0.08𝜎 and 15.7%, in Wang et al. [29],(5) respectively. For the thorax width, no paper has reported its estimation based on radar measurement. This is the first time that the thoraxwidth𝑚𝑚 width = has −4.41𝜆 been extracted+ 0.69𝜆 from−0.03𝜆 multi-frequency. RCSs with high(6) accuracy. The aspect ratio is also an important feature used to characterize insect shape, and this shape parameterTable 1. Morphological can be expected data to of provide measured further species information and scattering on the features target’s extracted identity. from Therefore, multi- the aspectfrequency ratio is also RCSs. estimated based on the measured body length and thorax width. The distribution of the estimated aspect ratio versus the true aspect ratio is shown in Figure5. The aspect ratio has an Mass Body Length Thorax Width estimationMethod error of 7.99%. The best-fitting lines of insect mass, body length and thorax width can be 𝑟 1 2 3 𝑟 𝑟 respectively represented (p as ) RMSPE (p) RMSPE (p) RMSPE 0.9526 0.8836 𝜆 16.31% 4 3 11.25%2 0.916 (<0.001) 13.37% mass(<0.001)(mg ) = 771.81λmax + 125.54(<0.001)λmax 7.23λmax + 0.17λmax, (4) 0.9464 − 0.8927 − 0.8575 𝜎 23.53% 3 10.74%2 20.86% (<0.001)log [length(mm)] = 54.31(<0.001)σ + 2.94σ + 0.08σmax(<0.001), (5) 10 − max max 1 2 3 𝑟 is the Spearman's rank correlation coefficient;3 p means2 p-value; RMSPE represents root mean width(mm) = 4.41λmax + 0.69λmax 0.03λmax. (6) square percentage error (see Appendix− A). −

Figure 5. Relation of the estimated aspect ratio to true aspect ratio. The green dots depict the estimated Figure 5. Relation of the estimated aspect ratio to true aspect ratio. The green dots depict the estimated aspect ratio. The black line depicts the reference line. aspect ratio. The black line depicts the reference line. We also assessed the traditional mass estimation method with the laboratory-measured RCSs from a publishedWe also database assessed following the traditional the same mass process. estimation A data setmethod including with 57the insects laboratory-measured was taken as the RCSs test input.from Thea published data originated database from following two separate the same data proc sources:ess. A RCS data data set ofincluding 43 insects 57 (massinsects ranging was taken from as 45the to 648test mg)input. measured The data by originated Aldhous [33 from], denoted two sepa hererate as A,data and sources: RCS data RCS of 14data insects of 43 (mass insects ranging (mass fromranging 9 to 254from mg) 45 measuredto 648 mg) by measured Wolf et al. by [ 30Aldhous], denoted [33], here denoted as W. here Based as on A, laboratory and RCS data measurements of 14 insects of(mass RCS andranging mass, from Chapman 9 to 254 presentedmg) measured two empiricalby Wolf et equationsal. [30], denoted to estimate here as the W. mass Based from on laboratory the RCS parametersmeasurements [24]. Theof RCS calculated and mass, RMSPE Chapman is 0.446 presente for datad Atwo and empirical 0.474 for equations data W, which to estimate are both the three mass timesfrom as the large RCS as parameters the value based[24]. The on thecalculated new method. RMSPE We is 0.446 also analyzedfor data A the and distributions 0.474 for data of W, percent which relativeare both error three (PRE, times see Appendixas large Aas) forthe the value three ba datasetssed on abovethe new (Figure method.6). Figure We 6also shows analyzed that the the measurementdistributions results of percent for our relative data error are more (PRE, concentrated see Appendix in theA) for area the of three small datasets errors, indicating above (Figure that a6). high estimation accuracy of the mass can be achieved for most insects based on the proposed method.

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Figure 6 shows that the measurement results for our data are more concentrated in the area of small Remote Sens. 2019, 11, 1977 9 of 19 errors, indicating that a high estimation accuracy of the mass can be achieved for most insects based on the proposed method.

(a)

(b)

(c)

Figure 6. Distributions of percent relative errors. (a) Data measured by Aldhous [33], (b) data measured Figure 6. Distributions of percent relative errors. (a) Data measured by Aldhous [33], (b) data by Wolf et al. [30], and (c) our data. The definition of percent relative error is given in AppendixA. measured by Wolf et al. [30], and (c) our data. The definition of percent relative error is given in 3. SpeciesAppendix Identification A. of Migratory Insects Insect identification with radars is critically important for insect migration research. The ability to 3. Species Identification of Migratory Insects identify a target has many application prospects for using radars as entomological tools [30]. However, establishingInsect aidentification radar classification with radars scheme is basedcritically on the important present knowledgefor insect migration of radar measurements research. The would ability beto inadequateidentify a [target30]. We has stated many that application morphological prospe featurescts for can using be usedradars to discriminateas entomological different tools insect [30]. speciesHowever, [39]. establishing We proved thata radar additional classification morphological scheme features based on can the be extractedpresent knowledge from entomological of radar radarmeasurements data, yet lacking would thebe multi-frequencyinadequate [30]. capabilityWe stated at that this morphological moment makes features it harder can to implementbe used to insectdiscriminate identification. different In insect the following species [39]. section We weprov exploreed that the additional identification morphological performance features of various can be morphologicalextracted from parameters. entomological radar data, yet lacking the multi-frequency capability at this moment makes it harder to implement insect identification. In the following section we explore the 3.1.identification Data Description performance of various morphological parameters. In this study, insect classification between 23 species of 5532 total insects is carried out. All insects 3.1. Data Description were caught using a search-light trap at Beihuangcheng Island, in Bohai Gulf, China, from August to OctoberIn this 2015. study, Beihuangcheng insect classification island between has been 23 proven species toof be5532 an total important insects stop is carried for migratory out. All insectsinsects and were birds caught in the using migration a search-light corridor betweentrap at Beihuangcheng Shandong Peninsula Island, and in LiaodongBohai Gulf, Peninsula China, from [40]. TheAugust species, to October quantities 2015. and Beihuangchen measured thoraxg island widths has been are proven listed in to Tablebe an2 important. The other stop morphological for migratory parametersinsects and (i.e., birds mass, in the body migration length, aspectcorridor ratio between and wingbeat Shandong frequency) Peninsula were and publishedLiaodong inPeninsula former research[40]. The [39 ].species, The number quantities 1 to 23 and is representative measured th oforax the species.widths Allare of listed the species in Table are . 2. The Among other them,morphological seven species parameters were trapped (i.e., mass, with lessbody than length, 50 individuals. aspect ratio Dueand towingbeat the limited frequency) quantities were of thesepublished species, in aformer data extensionresearch [39]. approach The number was adopted 1 to 23 to is extend representative each species of the to 5000species. samples All of [39 the]. Definitespecies measurementare moths. Among errors them, were introducedseven species into were the extensiontrapped with dataset less to than evaluate 50 individuals. the identification Due to performance.the limited quantities Specifically, of these the species, constructed a data measurement extension approach deviations was ofadopted morphological to extend parameterseach species (i.e.,to 5000 mass, samples body length, [39]. Definite thorax width, measurement aspect ratio errors and were wingbeat introduced frequency) into werethe extension 17%, 11%, dataset 14%, 8% to andevaluate 1 Hz, respectively,the identification which performance. is consistent Specific with theally, estimation the constructed precision measurement of the proposed deviations parameter of estimationmorphological method. parameters Next, the (i.e., new mass, dataset body was length, used thorax for further width, analysis. aspect ratio and wingbeat frequency) were 17%, 11%, 14%, 8% and 1 Hz, respectively, which is consistent with the estimation precision of the proposed parameter estimation method. Next, the new dataset was used for further analysis.

Remote Sens. 2019, 11, 1977 10 of 19

Table 2. The primary information of the 23 insect species.

Thorax Width (mm) Label Species Family Order Quantity Range Mean Std. Dev. 1 Eriopyga grandis Noctuidae 473 2–5 3.35 0.68 2 Agrotis tokionis Noctuidae Lepidoptera 382 5–9 7.11 0.84 3 Xestia c-nigrum Noctuidae Lepidoptera 43 5–7 5.47 0.55 4 Agrotis praecox Noctuidae Lepidoptera 78 5–6 5.70 0.46 5 Spodoptera litura Noctuidae Lepidoptera 129 3–5 3.86 0.54 6 Heliothis dipsacea Noctuidae Lepidoptera 84 2–5 3.58 0.54 7 Speiredonia retorta Noctuidae Lepidoptera 32 3–5 3.91 0.69 8 Dermaleipa juno Noctuidae Lepidoptera 61 10–14 11.52 0.89 9 Acronicta rumicis Noctuidae Lepidoptera 58 2–3 2.97 0.18 10 Calospilos suspecta Geometridae Lepidoptera 147 2–3 2.03 0.16 11 Spilarctia subcarnea Arctiidae Lepidoptera 296 2–6 4.33 0.70 12 Spilosoma niveus Arctiidae Lepidoptera 48 4–6 5.46 0.54 13 Amsacta lactinea Arctiidae Lepidoptera 27 4–6 5.26 0.53 14 Rhyparioides amurensis Arctiidae Lepidoptera 144 3–5 3.78 0.43 15 bilineata Lepidoptera 53 8–16 12.34 1.75 16 Sphingidae Lepidoptera 29 7–10 8.52 0.63 17 rubiginosa Sphingidae Lepidoptera 41 7–11 9.24 0.77 18 Callambulyx tartarunovii Sphingidae Lepidoptera 35 12–15 13.91 0.95 Macroglossum 19 Sphingidae Lepidoptera 84 10–14 12.37 0.77 stellatarum 20 sticticalis Pyralididae Lepidoptera 892 1–2 1.37 0.48 21 Spoladea recurvalis Pyralididae Lepidoptera 1574 1–2 1.02 0.13 22 Pantala flavescens Libellulidae Odonata 768 11–14 12.39 0.72 23 Enallagma cyathigerum Coenagriidae Odonata 54 4–6 5.28 0.56

3.2. Classification Method The species identification of migratory insects is a multi-classification problem. Machine learning algorithms can be selected to solve the problem. Support vector machine (SVM) was originally put forward to solve binary classification problems with the advantages of global optimization, a simple structure and high practicability [41–43]. The SVM algorithm has been widely used in many research fields and works well with limited training samples [44]. Mayo et al. demonstrated that the algorithm SVM achieved the best result in the identification of 35 species of moths compared with four other machine learning methods (Bayes, instance-based learning, decision trees and random forests) [45]. Focusing on the classification issues in our study, the SVM algorithm is selected. In the classification process, SVM defines a maximal margin hyperplane in the feature space to separate two labeled targets [41]. To classify non-linearly separable classes, the radial basis function (RBF) is selected due to its superiority over other functions [46]. SVM is a binary classifier. To solve the multi-classification task, the decision-tree-based support vector machine (DTSVM) method was adopted [47]. DTSVM is a formulation of the SVM algorithm that converts a multi-class problem into the integration of several binary classification problems using a tree structure [47]. As shown in Figure7, a binary tree is developed based on a certain separability measure. Four maximal margin hyperplanes should be calculated for the five-class classification problem. Remote Sens. 20192019,, 1111,, 1977x FOR PEER REVIEW 11 of 19 Remote Sens. 2019, 11, x FOR PEER REVIEW 11 of 19

{A,B,C,D,E} {A,B,C,D,E}

{A,B} {C,D,E} {A,B} {C,D,E}

{D,E} {D,E}

A BCDE A BCDE

Figure 7. Decision-tree-based supportsupportvector vector machine machine (DTSVM) (DTSVM) classifier. classifier. The The capital capital letters letters (i.e., (i.e., A, A, B, C,B,Figure C, D andD 7.and E)Decision-tree-based E) represent represent diff differenterent support targets. targets. vector The The circles machinecircles represent represent (DTS binaryVM) binary classifier. SVM SVM classifiers. Theclassifiers. capital letters (i.e., A, B, C, D and E) represent different targets. The circles represent binary SVM classifiers. 3.3. Separability Analysis of the Thorax Width 3.3. Separability Analysis of the Thorax Width 3.3. SeparabilityWe reported Analysis that the of mass,the Thorax body Width length, aspect ratio and wingbeat frequency can be combined We reported that the mass, body length, aspect ratio and wingbeat frequency can be combined to discriminate different insect species, and the distributions and separability of these four features to discriminateWe reported different that the insect mass, species, body length, and the aspect distributions ratio and and wingbeat separability frequency of these can fourbe combined features have been fully analyzed and discussed [39]. In this paper, we proved that the thorax width could haveto discriminate been fully differentanalyzed insect and discussed species, and [39]. the In dithstributionsis paper, we and proved separability that the of thorax these fourwidth features could also be extracted from the radar signal with a high level of accuracy. The thorax width is a usual and alsohave be been extracted fully analyzed from the radarand discussed signal with [39]. a high In th levelis paper, of accuracy. we proved The thatthorax the width thorax is width a usual could and important feature for characterizing insect size and is also easy to measure. Therefore, it is necessary to importantalso be extracted feature from for characterizing the radar signal insect with size a high and level is also of easyaccuracy. to measure. The thorax Therefore, width itis is a necessaryusual and combine the thorax width for further analysis of insect identification. toimportant combine feature the thorax for characterizingwidth for further insect analysis size and of insectis also identification. easy to measure. Therefore, it is necessary For the intuitive analysis of the thorax width distribution, the median and min–max range are to combineFor the the intuitive thorax analysis width for of furtherthe thorax analysis width of distribution, insect identification. the median and min–max range are shown in Figure8. The thorax width of all specimens ranges from 1 to 16 mm. The distribution range shownFor in the Figure intuitive 8. The analysis thorax widthof the ofthorax all specimens width distribution, ranges from the 1 tomedian 16 mm. and The min–max distribution range range are of the thorax width is relatively narrow for most species. ofshown the thorax in Figure width 8. The is relatively thorax width narrow of allfor specimens most species. ranges from 1 to 16 mm. The distribution range of the thorax width is relatively narrow for most species.

Figure 8. The distributions of of the the thorax thorax width width of of all all trapped trapped species. species. Each Each line line segment segment represents represents a aspecies.Figure species. 8. The The The numbers distributions numbers from from of 1 the 1to to thorax23 23 in in the thewidth horizontal horizontal of all trapped axis axis denote species. species Each line labels. segment The verticalrepresents axis a representsspecies. The thethe numbers rangerange ofof thefromthe thoraxthorax 1 to width.width.23 in the horizontal axis denote species labels. The vertical axis represents the range of the thorax width.

Remote Sens. 2019, 11, 1977 12 of 19 Remote Sens. 2019, 11, x FOR PEER REVIEW 12 of 19

TheThe thorax thorax width width probability probability distribution distribution curves curves of of the the extended extended data data are are shown shown in in Figure Figure 99.. The probability probability distribution curve of the the thorax thorax width width of of each each specie speciess follows follows a a Gaussian Gaussian distribution, distribution, which is is in in accordance accordance with with the the actual actual situation. situation. The The range range of the of the thorax thorax width width overlaps overlaps severely severely for somefor some species, species, which which cannot cannot be beidentified identified based based only only on on the the thorax thorax width. width. However, However, some some other other speciesspecies can can be be distinguished distinguished based based only only on on the the thorax thorax width, width, so so the the thorax thorax width width can can also also be be used used as as anan identification identification feature feature that that may may contribute contribute to to further further improvement improvement of of the the identification identification accuracy. accuracy. InIn the the subsequent subsequent section, section, the thorax width is used to identify insects incorporating other features.

Figure 9. The probability distribution curves of the thorax width. Note: Different colors correspond to Figuredifferent 9. species.The probability distribution curves of the thorax width. Note: Different colors correspond to different species. 3.4. Classification Performance Achieved Using All Features 3.4. Classification Performance Achieved Using All Features The identification performance of insect species based on the selected insect morphological parametersThe identification (i.e., mass, body performance length, thorax of insect width, species aspect based ratio andon the wingbeat selected frequency) insect morphological was analyzed parametersin this section. (i.e., Based mass, on body the DTSVMlength, thorax algorithm, width, the aspect classification ratio and scheme wingbeat was frequency) built using was the measuredanalyzed inmorphological this section. featuresBased on (Figure the DTSVM10). Twenty-two algorithm, binary the SVMclassification classifiers scheme are needed was for built the 23using species. the measuredIn classification, morphological the class features that is farthest (Figure from 10). Twen the remainingty-two binary classes SVM is recursively classifiers are separated needed until for the all 23classes species. are separated.In classification, the class that is farthest from the remaining classes is recursively separatedExtension until samplesall classes were are separated. randomly attributed to either a training or testing set. The training set wasExtension used to train samples the binary were randomly SVM classifiers attributed in the to classificationeither a training scheme or testing (Figure set. 10 The). Oncetraining all theset wasbinary used SVM to classifierstrain the binary were trained,SVM classifiers the testing in databasethe classification of 57,500 scheme migratory (Figure insects 10). was Once imported all the binaryto evaluate SVM theclassifiers classification were trained, performance. the testing Then, data thebase identification of 57,500 migratory probability insects of each was insect imported species to evaluatewas calculated. the classification performance. Then, the identification probability of each insect species was calculated.The classification results are shown in Figure 11. The correct identification probabilities are higher than 0.5 for all of the 23 species, which means that we have an acceptable recognition ability for all of the species. In addition, the correct identification probabilities are higher than 0.8 for 15 of the 23 species (~65%), indicating that we have a reasonably high recognition ability for about half the species. The average correct identification probability is 0.85.

Remote Sens. 2019, 11, x FOR PEER REVIEW 13 of 19

SVM

Species Remote Sens. 2019, 11, 1977 13 of 19 Remote Sens. 2019, 11, x FOR PEER REVIEW 13 of 19

SVM

Species

Figure 10. The classification scheme for the 23 species of migratory insects based on wingbeat frequency, mass, body length, aspect ratio and thorax width. Note: The blue circles correspond to insect species and green circles correspond to binary SVM classifiers.

The classification results are shown in Figure 11. The correct identification probabilities are higher than 0.5 for all of the 23 species, which means that we have an acceptable recognition ability for all Figureof the species. 10.10. TheThe classification Inclassification addition, scheme thescheme correct for for the 23identificationthe species 23 species of migratory probabilitiesof migratory insects basedinsectsare higher on based wingbeat than on 0.8 frequency,wingbeat for 15 of the 23 speciesfrequency,mass, body (~65%), mass, length, indicatingbody aspect length, ratio that aspect and we thorax haveratio width.aand reasonably th Note:orax width. The high blue Note: recognition circles The correspond blue ability circles tofor correspond insect about species half to the and green circles correspond to binary SVM classifiers. species.insect The speciesaverage and correct green identificationcircles correspond probability to binary isSVM 0.85. classifiers.

The classification results are shown in Figure 11. The correct identification probabilities are higher than 0.5 for all of the 23 species, which means that we have an acceptable recognition ability for all of the species. In addition, the correct identification probabilities are higher than 0.8 for 15 of the 23 species (~65%), indicating that we have a reasonably high recognition ability for about half the species. The average correct identification probability is 0.85. cation accuracy fi Identi cation accuracy fi

Identi FigureFigure 11. 11. IdentificationIdentification accuracy accuracy for for each each of ofthe the 23 23species species based based on onwingbeat wingbeat frequency, frequency, mass, mass, body body length,length, aspect aspect ratio ratio and and thorax thorax width. width.

TheThe normalized normalized confusion confusion matrixmatrix ofof the the classification classification results results is shown is shown in Figure in Figure 12, which 12, describeswhich describeshow many how species many werespecies correctly were classifiedcorrectly classified and how manyand how species many were species incorrectly were classifiedincorrectly for classifiedeach of thefor categories.each of the Thecategories. diagonal The of diagonal the matrix of represents the matrix the represents correct classification the correct classification probability of probabilityeachFigure species, of 11. each i.e.,Identification species, it is classified i.e., accuracy it tois classified itself.for each Each of to the speciesitself. 23 species Each is more based species likelyon wingbeatis more to be likely classifiedfrequency, to be tomass, classified itself body than to to itselfother thanlength, species, to aspectother which ratiospecies, verifies and whichthorax the correctness width.verifies the of correctness the classification of the method.classification In addition, method. some In addition, confusion occurred between the species Agrotis tokionis (Butler) (Species No.: 3) and Xestia c-nigrum (Linnaeus)

(SpeciesThe No.:normalized 4). This confusion is because matrix these two of the species classification are similar results in the bodyis shown shape in andFigure belongs 12, which to the describessame family how (i.e., many Lepidoptera: species were Noctuidae). correctly classified and how many species were incorrectly classified for each of the categories. The diagonal of the matrix represents the correct classification probability of each species, i.e., it is classified to itself. Each species is more likely to be classified to itself than to other species, which verifies the correctness of the classification method. In addition,

Remote Sens. 2019, 11, x FOR PEER REVIEW 14 of 19 some confusion occurred between the species Agrotis tokionis (Butler) (Species No.: 3) and Xestia c- nigrum (Linnaeus) (Species No.: 4). This is because these two species are similar in the body shape and belongs to the same family (i.e., Lepidoptera: Noctuidae). For comparative analysis, a simulation using the measurement precision of existing methods (insect mass uncertainty 24%, insect body length uncertainty 16% and wingbeat frequency deviation 1Hz) was also conducted, which produced an average correct identification probability of only 0.72. Therefore, the thorax width and aspect ratio are also effective features for insect identification. To summarize,Remote Sens. 2019 the, 11 ,above 1977 statistics show that we can identify migratory insects with the current14 of 19 parameter measurement accuracy.

1 1 2 3 0.9 4 5 0.8 6 7 0.7 8 9 10 0.6 11 12 0.5 13 14 0.4 15 16 17 0.3 18 19 0.2 20 21 0.1 22 23 0 1234567891011121314151617181920212223 Predicted Species No.

Figure 12. Normalized 23 23 confusion matrix of classification results. The value of the element Figure 12. Normalized 23 ×× 23 confusion matrix of classification results. The value of the element in in the matrix that has a row number of i and a column number of j (i,j = 1,2, ... ,23) represents the the matrix that has a row number of i and a column number of j (i,j = 1,2,…,23) represents the probability that insect i is considered to be insect j after the classification process. probability that insect i is considered to be insect j after the classification process. For comparative analysis, a simulation using the measurement precision of existing methods 3.5.(insect Classification mass uncertainty Performance 24%, Ac insecthieved body Using length Single uncertainty Features 16% and wingbeat frequency deviation 1Hz)Using was alsomultiple conducted, features which may producedresult in a an good average classification correct identification performance. probability Nevertheless, of onlythe evaluation0.72. Therefore, of the the significance thorax width of single and aspect features ratio is also are alsoimportant, effective therefore features it for is insectnecessary identification. to assess theTo summarize,classification the performance above statistics based show on that indepe we canndent identify features. migratory The multi-class insects with classification the current algorithmparameter of measurement the SVM was accuracy. used here to solve the multi-class classification problems based on single features [40]. 3.5. Classification Performance Achieved Using Single Features The classification results, as listed in Table 3, reveal the degree of relevancy of these features to the givenUsing classification multiple features problem. may Notably, result in no a gooderror classificationwas added to performance. the single features Nevertheless, during the trainingevaluation and of testing the significance phases in of this single section. features In cont is alsorast, important, we ascertained therefore that it mass is necessary is the most to assess critical the featureclassification for insect performance discrimination, based onand independent the identifica features.tion accuracy The multi-class based on mass classification is much algorithmhigher than of thatthe SVMbased was on usedthe other here features to solve because the multi-class the mass classification distribution problems ranges of based different on single insect features species [vary40]. dramatically.The classification Based only results, on insect as listed mass, in the Table correct3, reveal identification the degree probabilities of relevancy ofare these higher features than 0.5 to thefor 18given of the classification 23 species problem. (~78%). Notably,The body no length error wasand addedthorax to width the single are the features second during and thethird training most importantand testing features, phases inrespectively. this section. However, In contrast, the we aspect ascertained ratio contributed that mass isthe the least most to criticalthe identification feature for comparedinsect discrimination, with the andother the identificationmorphological accuracy features. based Body on masslength is much and higherthorax than width that basedcan be on approximatelythe other features assumed because to thebe more mass intuitive distribution and ranges practical of diforff erentspecies insect identification species vary compared dramatically. with theBased aspect only ratio. on insect The mass,wingbeat the correctfrequency identification was also proven probabilities to be arean higherimpactful than feature 0.5 for for 18 insect of the classification.23 species (~78%). The body length and thorax width are the second and third most important features, respectively. However, the aspect ratio contributed the least to the identification compared with the other morphological features. Body length and thorax width can be approximately assumed to be more intuitive and practical for species identification compared with the aspect ratio. The wingbeat frequency was also proven to be an impactful feature for insect classification. RemoteRemote Sens.Sens.2019 2019,,11 11,, 1977 x FOR PEER REVIEW 1515 ofof 1919

Table 3. Identification accuracy for each of the 23 species based on single features. Table 3. Identification accuracy for each of the 23 species based on single features. Identification Accuracy Label Wingbeat Frequency MassIdentification Body Le Accuracyngth Thorax Width Aspect Ratio Label 1 Wingbeat Frequency0 Mass 0.804 Body0.8576 Length Thorax0.0052 Width Aspect0.1184 Ratio 2 0.3228 0.9928 0.0332 0.0144 0.6388 1 3 00 0.8040.8628 0.85760.0616 0.0256 0.0052 0.11840 2 0.3228 0.9928 0.0332 0.0144 0.6388 4 0 0.6608 0.5372 0.2388 0.6532 3 0 0.8628 0.0616 0.0256 0 5 0 0.9648 0.0396 0 0.0228 4 0 0.6608 0.5372 0.2388 0.6532 6 0.118 0.9436 0.2248 0 0.1464 5 0 0.9648 0.0396 0 0.0228 6 7 0.58040.118 0.9436 0.1428 0.22480 0.1848 0 0.14640 7 8 0.58040.6884 0.1428 0.8952 0.8832 0 0.8132 0.1848 0.5088 0 8 9 0.68840.012 0.8952 0.8564 0.88320.7596 0.7692 0.8132 0.50881 9 10 0.0120.226 0.8564 0.8852 0.75960.3772 0.776 0.7692 1 1 1011 0.2260 0.8852 0.8632 0.37720.46 0.7760 0.2188 1 1112 0.40560 0.8632 0.9724 0.608 0.46 0 0 0.078 0.2188 1213 0.40560.6932 0.9724 0 0.6080.68 0.0556 0 0.1952 0.078 1314 0.69320.0432 0.8492 0 0.4248 0.68 0.0556 0 0.7172 0.1952 1415 0.04320.9272 0.8492 0.4732 0.42480.0304 0.2644 0 0.0052 0.7172 15 0.9272 0.4732 0.0304 0.2644 0.0052 16 0.7876 0.9304 0.1628 0.0224 0.6488 16 0.7876 0.9304 0.1628 0.0224 0.6488 17 0.6764 0.9992 0.7688 0.028 0.5936 17 0.6764 0.9992 0.7688 0.028 0.5936 1818 0.4740.474 0.7544 0.7544 0.9104 0.5156 0.5156 0.75 0.75 1919 0.99960.9996 0.4964 0.4964 0.2896 0.5052 0.5052 0.6272 0.6272 2020 00 0.8332 0.8332 0.9276 0.03 0.03 0.468 0.468 2121 00 1 1 0.986 0.618 0.618 1 1 2222 11 0.3928 0.3928 1 0.566 0.566 0.0636 0.0636 2323 0.9960.996 0.9376 0.9376 0.1632 0.3812 0.3812 0.1632 0.1632

TheThe correct correct classification classification rates rates of di ffoferent different insects in basedsects onbased independent on independent features are features also graphically are also showngraphically in Figure shown 13. in The Figure results 13. rise, The fall results and di rise,ffer fall considerably and differ when considerably different when features different are used. features For a certainare used. species, For a itscertain correct species, classification its correct rate classifica can rangetion from rate 0 can to 1 range based from on di ff0erent to 1 based features. on different Some of thefeatures. species Some can beof undoubtedlythe species can identified be undoubtedly only by usingidentified certain only features. by using However, certain features. if all single However, feature identificationif all single feature algorithms identification are just combined,algorithms withoutare just featurecombined, ranking, without it may feature not ranking, result in improvedit may not identificationresult in improved probabilities. identification probabilities.

1 (a) 0.5 0 1234567891011121314151617181920212223 1 (b) 0.5 0 1234567891011121314151617181920212223 cation Rate 1 fi (c) 0.5 0 1234567891011121314151617181920212223 1 (d) 0.5

Correct Classi Correct 0 1234567891011121314151617181920212223 1 (e) 0.5 0 1234567891011121314151617181920212223 Species No.

Figure 13. Correct classification rates based only on a single feature: (a) wingbeat frequency, (b) mass, (Figurec) body 13. length, Correct (d) classification thorax width, rates and based (e) aspect only ratio. on a single feature: (a) wingbeat frequency, (b) mass, (c) body length, (d) thorax width, and (e) aspect ratio.

Remote Sens. 2019, 11, 1977 16 of 19

4. Discussion The simple and comprehensive relationships between an insect’s morphological parameters and scattering features, to a certain extent, reflect the different scattering mechanisms of insects as dielectric scatterers in the Rayleigh and resonance regions. The experimental results show that the insect’s RCSs has obvious fluctuations with the variation of frequencies even in the Rayleigh region, which might be caused by the insect’s complex body composition. Therefore, the estimation of morphological parameters based on a single frequency may have a negative impact on the accuracy and robustness of the result. In addition, the insects have more complex organism structures and shapes in contrast to the selected digital model, and thus more complex shapes and components should be considered in order to make the model closer to reality. The reliable identification of radar targets is a prerequisite for using radars as a research instrument. Species identification of airborne insects will help biologists and entomologists conduct research on subclasses or even exact categories and interspecific behavioral characteristics. Additionally, if the species of migrating pests can be known, people can apply more targeted prevention and control measures. Given the lack of identification ability, migratory insects studied with the traditional use of radars are only differentiated between broad categories [48], or even without any classification (only with birds or raindrops) [49], which has seriously affected further analysis of the observed results. Therefore, the species identification of migratory insects requires further analysis. We proved that the morphological parameters can be used to discriminate different species with high accuracy based on the DTSVM algorithm. Given that the morphological parameters can be extracted from multi-frequency RCSs of insects, we assume that the multi-frequency RCSs can also be used to identify species of migratory insects directly. Therefore, the identification of insect species based on multi-frequency RCSs should be studied in the future, although more data are needed for this. In addition, if entomological radars have the ability to acquire fully polarized data, then other useful parameters can be acquired and estimation accuracy can be improved, which would contribute to a higher species identification accuracy.

5. Conclusions In this paper, an improved method for the measurement of insect mass and body length using multi-frequency RCSs was proposed, and the lowest estimation errors of the mass and body length were ~17% and ~11%, respectively. The thorax width and aspect ratio can also be extracted from the radar signal with estimation errors of ~14% and ~8%, respectively. Then, the identification performance is evaluated based on these four features and the wingbeat frequency under the achievable estimation precision of the proposed parameter estimation method. The correct identification probabilities are higher than 0.5 for all of the 23 species and are higher than 0.8 for 15 of the 23 species. Finally, we evaluated the classification performance using independent features and found that insect mass is the most effective feature for discriminating different insect species. These findings provide improvements in individual parameter measurement for insect radars and imply the possibility of higher precision species identification, which would promote the biomass quantification of over-flying migratory insects to enable studying the role of insect migration in ecosystems and pest control.

Supplementary Materials: The following are available online at http://www.mdpi.com/2072-4292/11/17/1977/s1: Table S1: X-band RCSs of measured insects (unit dBsm), Table S2: K-band RCSs of measured insects (unit dBsm), Table S3: Physical data of measured species and scattering features extracted from multi-frequency RCSs. Author Contributions: C.H. and S.K. conceived and designed the methods; S.K. and F.Z. performed the simulation; R.W. supplied technical support for the experimental configuration; S.K., R.W. and C.H. wrote the manuscript based on input from all authors; and all authors provided editorial advice. Funding: This research was funded by the Special Fund for Research on National Major Research Instruments (Grant No. 31727901). Conflicts of Interest: The authors declare no conflict of interest. Remote Sens. 2019, 11, 1977 17 of 19

Appendix A

The Spearman’s rank correlation coefficient (denoted as rs), a nonparametric measure of the rank values of two variables, was calculated to evaluate the correlation between the estimated and true values of the morphological parameters [50]. The Spearman’s correlation evaluates the monotonic relationships between two variables, even if their relationship is not linear [50]. The p-value is also calculated to test the null hypothesis of no correlation. The root mean square percentage error (RMSPE) was used to evaluate the deviations between the true values and the estimates [51]. A smaller value of the RMSPE indicates a higher mean estimation accuracy. The percent relative error (PRE) was used to evaluate the estimation error of each insect. A smaller value of the MRE indicates a higher estimation accuracy. The Spearman’s rank correlation coefficient is defined as [50]

N P 2 6 di i=1 rs = 1 (A1) − N3 N − where di is the difference between the two ranks of XM and XR. The RMSPE is defined as [51] uv t N !2 1 X XM[i] XR[i] RMSPE = − 100% (A2) N X [i] × i=1 R where XM and XR are estimates and true values, respectively, and N is the number of specimens. The PRE is defined as [52]

XM[i] XR[i] PRE = − 100%. (A3) XR[i] ×

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