Application and Evaluation of the Gaofen-3 Satellite on a Terrain Survey with Insar Technology

applied sciences

Article Application and Evaluation of the Gaofen-3 Satellite on a Terrain Survey with InSAR Technology

Yuzhi Zheng, Zhenwei Chen and Guo Zhang *

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China; [email protected] (Y.Z.); [email protected] (Z.C.) * Correspondence: [email protected]; Tel.:+86-139-0718-2592

Received: 7 December 2019; Accepted: 21 January 2020; Published: 23 January 2020

Abstract: The Gaofen-3 satellite is the first SAR satellite independently developed in China that achieves interferometric imaging and measurement, which improves upon Chinese civil SAR satellite data. To verify the ability of the Gaofen-3 satellite’s InSAR technology, we acquired data from Dengfeng, China, to evaluate the application and accuracy of an InSAR terrain survey. To reduce the effects introduced by data processing of Gaofen-3 data, high-accuracy InSAR image pair co-registration and phase filtering methods were adopted. Six GCPs data and 1:2000-scale DEM data were used to evaluate the elevation accuracy. In addition, for comparison with other satellites, we processed the dataset of the same area acquired by the non-civilian Yaogan-29 satellite with the same methods and evaluated the results. The experimental results indicated that the interferometric data of the Gaofen-3 satellite can achieve an accuracy of higher than 4 m of interferometric height measurement. Therefore, it will have broad prospects in the domestic InSAR application. Our research provides a certain value for the reference of the development of InSAR sensors in China.

Keywords: Gaofen-3 satellite; InSAR; height measurement; accuracy evaluation

1. Introduction Interferometric SAR (InSAR) technology, an important application technique for SAR satellites for earth observation, can be used for height measurement and surface deformation monitoring [1–6]. Currently, there are many international satellites available for the regular use of InSAR technology. However, China is only at the beginning of this stage. Gaofen-3 is a synthetic aperture radar (SAR) satellite developed by the China Academy of Space Technology and was launched with a CZ-4 carrier rocket from Taiyuan Satellite Launch Center on 10 August 2016 [2]. Table1 shows the basic parameters of InSAR satellites globally and Gaofen-3 in particular [2,7–15]. Gaofen-3, as a domestic SAR satellite that achieves interferometric imaging for the first time in China’s civilian field, is of great significance. The satellite platform was equipped with a C-band SAR sensor, which can acquire high-resolution SAR images based on the phased array system. The observation width of Gaofen-3 is 10–650 km and the spatial resolution is 1–500 m. It has 12 imaging modes, including band, scanning, sliding beam, double aperture superfine and wave imaging modes as well as global observation imaging mode for ocean applications. It has a high resolution, large imaging width and multi-imaging modes. Furthermore, it can expand the observation range and improve the fast response capability by left and right position maneuvering. Table2 lists the imaging modes and parameters of Gaofen-3 [2]. At present, the research on Gaofen-3 is mainly focused on the improvement of its geometric positioning accuracy [16], the application of ocean wind or wave retrieval [17,18] and ship detection [19]. However, there is a lack of research on the interferometric capability of Gaofen-3, especially the interferometric measurement of terrain elevation with its SAR images.

Appl. Sci. 2020, 10, 806; doi:10.3390/app10030806 www.mdpi.com/journal/applsci Appl. Sci. 2020, 10, 806 2 of 17

Table 1. Basic parameters of interferometric synthetic-aperture radar satellites globally and the Gaofen-3 satellite in particular.

Orbit Altitude Revisit Time Satellite Resolution (m) Launch Time Country/Area Band (km) (day) ERS-1/2 30 30 July 1991/April 1995 Europe 785/824 C 35 × JERS-1 18 18 February 1992 Japan 568 L 44 × Radarsat-1 9–100 November 1995 Canada 798 C 24 Envisat-1 10–1000 March 2002 Europe 768 C 35 ALOS-1 10–100 January 2006 Japana 692 L 46 TerraSAR-X 1–18.5 June 2007 Germany 514 X 11 June 2007 December 2007 COSMO-SkyMed 1–100 Italy 619 X 16 October 2008 November 2010 Radarsat-2 3–100 December 2007 Canada 798 C 24 TanDEM-X 1–16 June 2010 Germany 514 X 11 ALOS-2 (1–60) (3–100) May 2014 Japan 628 L 14 × Sentinel-1A/1B (5–20) (5–40) April 2014/April 2016 Europe 693 C 6 × Gaofen-3 1–500 August 2016 China 755 C 29

Table 2. Imaging modes and parameters of the Gaofen-3 satellite.

Incidence Angle Resolution (m) Observation Width Imaging Mode (◦) Range Azimuth (km) Spotlight 20~50 1.0–1.5 0.9–2.5 10 10 × Ultraﬁne strip 20~50 3 2.5–5 30 Fine strip I 19~50 5 4–6 50 Fine strip II 19~50 10 8–12 95–110 Standard strip 17~50 25 15–30 95–150 Narrow scanning 17~50 50~60 30–60 300 Wide scanning 17~50 100 50–110 500 Global observation 17~53 500 350–700 650 Full polarization 20~41 8 6–9 20–35 strip I Full polarization 20~38 25 15~30 35–50 strip II Wave 20~41 10 8~12 5 5 ×

This paper attempts to explore and evaluate the interferometric capability of Gaofen-3. It is necessary to examine whether the satellite can perform interferometry and to evaluate its interferometric measurement accuracy as well. In the study, an interferometric data set from the Dengfeng area, China, was used to obtain elevation maps. The data set was processed through a series of processing, including high-accuracy InSAR image co-registration, interferometry processing, and interferogram flattening, phase filtering, phase unwrapping and so on. Then two kinds of digital elevation model (DEM) data were used to compare and evaluate the accuracy of the interferometric measurement of Gaofen-3 satellite data, respectively. One is GCPs data from six corner reflectors in the study area. The other is 1:2000-scale digital elevation model (DEM) data, which was obtained from an aerial survey. Finally, for comparison with other SAR sensors and further analysis of the interferometric measurement capability of Chinese SAR satellites, we processed the dataset of the same area acquired by the Chinese non-civilian Yaogan-29 satellite with the same methods.

2. Experimental Dataset The coverage of the images used for the terrain measurement experiment in this study was located in Dengfeng City, Henan Province. The temporal baseline for the images was a repeat cycle, which is 29 days, and the spatial vertical baseline was approximately 589 m. Since there were different degreesAppl. Sci. 2020of surface, 10, 806 deformation within the coverage of the whole image during the period between3 of 17 two observations, we demarcated an area that did not contain surface deformation as the experimental area. The geographical coverage of the area is shown in Figure 1a, and the basic informationarea. The geographical is shown in coverage Table 3. of Figure the area 1b is shows shown t inhe Figure single1 a, look and complex the basic (SLC) information intensity is shown of the Gaofenin Table-3. data.Figure The1b shows elevation the single of the look experimental complex (SLC) area intensity was between of the Gaofen-3 300 and data. 1000 The m, elevation and the topographyof the experimental is shown area in Figure was between 1c. 300 and 1000 m, and the topography is shown in Figure1c.

(b) (a)

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Figure 1. Geographical coverage (Dengfeng area, China) of experimental dataset of the Gaofen-3 Figure(GF3) satellite 1. Geographical (a), single lookcoverage complex (Dengfeng intensity area, image China) from ofthe experimental GF-3 satellite dataset in the Dengfeng of the Gaofen area (b-3), (GF3)and the satellite corresponding (a), single distribution look complex of elevation intensity acquired image from by the the Shuttle GF-3 Radarsatellite Topography in the Dengfeng Mission area (c). (b), and the corresponding distribution of elevation acquired by the Shuttle Radar Topography Mission (c). Table 3. Gaofen-3 satellite data of experimental area (Dengfeng area, China). Master Image Slave Image Table 3. Gaofen-3 satellite data of experimental area (Dengfeng area, China). Date 29 November 2016 28 December 2016 Mode Master Image Fine stripSlave I Image ResolutionDate 29 November 2016 28 3 mDecember 2016 Wave length 5.56 cm (C band) Mode Fine strip I Path direction Descending Resolution 3 m 3. Interferometric Data Processing This section describes the major processing steps in obtaining a DEM with InSAR technology, using the interferometric data from Gaofen-3 in the Dengfeng area, and provides the basis for evaluating the interferometric imaging and measurement capabilities of Gaofen-3 satellite data. Regarding the application, in order to reduce the errors in data processing as much as possible, this study focused Appl. Sci. 2020, 10, 806 4 of 16

Wave length 5.56 cm (C band) Path direction Descending

3. Interferometric Data Processing This section describes the major processing steps in obtaining a DEM with InSAR technology, using the interferometric data from Gaofen-3 in the Dengfeng area, and provides the basis for evaluating the interferometric imaging and measurement capabilities of Gaofen-3 satellite data. Regarding the application, in order to reduce the errors in data processing as much as possible, this Appl. Sci. 2020, 10, 806 4 of 17 study focused on the interferometric image co-registration with a 0.01-pixel (equal to 3 cm) accuracy level and high-accuracy phase filtering methods to obtain higher accuracy for elevation onmeasurements. the interferometric image co-registration with a 0.01-pixel (equal to 3 cm) accuracy level and high-accuracy phase filtering methods to obtain higher accuracy for elevation measurements. 3.1. High-Accuracy InSAR Image Co-Registration 3.1. High-Accuracy InSAR Image Co-Registration Interferometric coherence can be guaranteed when the co-registration accuracy of the InSAR imageInterferometric pair is higher than coherence 0.1 pixel can [1 be]. However, guaranteed to improve when the the co-registration accuracy of the accuracy interferometric of the InSAR phase imageas well pair as is the higher elevation than 0.1 accuracy pixel [1 ]. of However, the final to reconstructed improve the accuracy DEM as of much the interferometric as possible, the phase co- asregistration well as the accuracy elevation of accuracy the image of the pair final in reconstructedthe experimental DEM area as much is required as possible, to reach the co-registrationa level of 0.01 accuracypixel. Although of the image an average pair in level the experimental of 0.1 pixel of area the is global required registration to reach aaccuracy level of 0.01of the pixel. image Although can be anobtained average using level ofthe 0.1 classical pixel of InSAR the global image registration co-registration accuracy method of the image [9], the can accuracy be obtained is difficult using the to classicalguarantee InSAR for local image are co-registrationas on the image, method especially [9], thethose accuracy with large is di topographicfficult to guarantee reliefs [ for20]. local areas on theThe image, classical especially method those is with not large recommended topographic for reliefs implementing [20]. the co-registration of the experimentalThe classical dataset. method To is achieve not recommended the 0.01 level for implementing of co-registration the co-registration accuracy in both of the the experimental range and dataset.azimuth, To this achieve study the adopted 0.01 level the of co-registration following co- accuracyregistration in both method. the range Firstly, and azimuth, the Shuttle this Radarstudy adoptedTopography the following Mission (S co-registrationRTM) DEM with method. a resolution Firstly, the of Shuttle1″ and Radarthe satellite Topography observation Mission geometric (SRTM) DEMparameters with a resolutionwere used ofto 1”eliminate and the satellitethe non- observationtranslation relationships geometric parameters between were images used due to eliminateto terrain thereliefs non-translation and inconsistent relationships observation between states. imagesSubsequently, due to terrainthe first reliefs co-registration and inconsistent was performed observation and states.the slave Subsequently, image was resampled. the first co-registration Afterwards, wasthe image performed pair had and simple the slave geometric image was transformation resampled. Afterwards,relations and the mainly image pair constant had simple offsets, geometric which transformationcould be fitted relations by global and co mainly-registration constant rational offsets, whichexpression could with be fitteda certain by globalnumber co-registration of offsets with rational high accuracy expression and reasonable with a certain distributions. number of In o ffordersets withto obtain high a accuracy series of and high reasonable-accuracy offsets, distributions. the high In-quality order to co obtain-registration a series points of high-accuracy were screened offsets, out thewith high-quality the signal-to co-registration-clutter-ratio (SCR) points and were intensity screened information out with the between signal-to-clutter-ratio the master and (SCR)resampled and intensityslave images. information The details between are provided the master by and Chen resampled [20], and slave the workflow images. The is shown details arein Figure provided 2. The by Chenmethod [20 ], was and applied the workflow to conduct is shown the in Figureco-registration2. The method of the was Gaofen applied-3 to experimental conduct the co-registration dataset in the ofDengfeng the Gaofen-3 area. experimentalThe co-registration dataset accuracy in the Dengfeng in range was area. 0.0121 The co-registration pixel and that accuracyin azimuth in was range 0.0093 was 0.0121pixel, which pixel and satisfied that in the azimuth expected was requirements. 0.0093 pixel, which satisfied the expected requirements.

orbits + DEM

Geometrical non-translation elimination

resampling polynomials: f1 Resample slave image

Compute SCR between master and salve

Detect points from SCR and amplitude map

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refined polynomials: f2 Compute transformation polynomials FigureFigure 2. 2. ImprovedImproved high-accuracy high-accuracy interferometricinterferometric synthetic-aperture synthetic-aperture radar radar co-registrationco-registration method method usedused inin thisthis study.study. DEM:DEM: digitaldigital elevationelevation model;model; SCR:SCR: signal-to-clutter-ratio.signal-to-clutter-ratio. 3.2. Interferometry Processing and Interferogram Flattening After fine registration and spectral offset filtering were conducted, an interferogram was generated Figure3a,c. There are two ways to generate a DEM using Gaofen-3 interferometric data. One is to use InSAR technology to calculate the terrain directly through the interferometric phase. The other is based on open-source data such as SRTM data and using the differential InSAR technique to process the elevation. In general, the latter can reduce the fringe density and is conducive to phase filtering and unwrapping. However, the resolution of the Gaofen-3 SAR data, in this case, was different from that of an open-source DEM, such as SRTM. For areas with large terrain reliefs, the difference in interferometric Appl. Sci. 2020, 10, 806 5 of 16

3.2. Interferometry Processing and Interferogram Flattening After fine registration and spectral offset filtering were conducted, an interferogram was generated Figure 3a,c. There are two ways to generate a DEM using Gaofen-3 interferometric data. One is to use InSAR technology to calculate the terrain directly through the interferometric phase. The other is based on open-source data such as SRTM data and using the differential InSAR technique to process the elevation. In general, the latter can reduce the fringe density and is conducive to phase filtering and unwrapping. However, the resolution of the Gaofen-3 SAR data, in this case, was different from that of an open-source DEM, such as SRTM. For areas with large terrain reliefs, the difference in interferometric phases contained dense fringes caused by different degrees of response of the two Appl.kinds Sci. 2020 of data, 10, 806 to the surface details. In addition, to verify the ability of Gaofen-3 satellite5 of 17 interferometric data to obtain the ground elevation more objectively, a DEM was produced directly through InSAR in this study, rather than using an external DEM for differential interferometry. phases contained dense fringes caused by diﬀerent degrees of response of the two kinds of data to the We subsequently performed interferogram flattening, and the results are shown in Figure 3b,d. surface details. In addition, to verify the ability of Gaofen-3 satellite interferometric data to obtain The coherence of the flattened interferogram was calculated using a 5 × 5 pixel window, and the the ground elevation more objectively, a DEM was produced directly through InSAR in this study, obtained coherence map is shown in Figure 4a; Figure 4b is the corresponding coherence probability rather than using an external DEM for diﬀerential interferometry. histogram.

(a) (b) -π

FigureFigure 3.3. Interferograms of of the the experimental experimental Gaofen Gaofen-3-3 satellite satellite data data in in the the Dengfeng Dengfeng area, area, China: China: (a) (ainterferogram,) interferogram, (b ()b flattened) ﬂattened interferogram, interferogram, (c ()c )local local part part of of the the interferogram, interferogram, ( d) locallocal partpart ofof thethe ﬂattenedflattened interferogram. interferogram.

We subsequently performed interferogram ﬂattening, and the results are shown in Figure3b,d. The coherence of the ﬂattened interferogram was calculated using a 5 5 pixel window, and the obtained × coherence map is shown in Figure4a; Figure4b is the corresponding coherence probability histogram. Based on the coherence analysis, the coherence of the Gaofen-3 interferometric data of the Dengfeng area was 0.413, and the standard deviation of the distribution was 0.186. Partial regions had geometric decorrelation due to terrain reliefs. The 29 days of time interval also resulted in certain degrees of time decorrelation. Appl. Sci. 2020, 10, 806 6 of 16

Appl. Sci. 2020, 10, 806 6 of 17 Appl. Sci. 2020, 10, 806 6 of 16

0.8

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0.6 Probability 0 0.2 (a) 0.4 (b) Figure 4. Coherence analysis of the flattened interferogram0 with (a) coherence map and (b) coherence 0.2 0 0.2 0.4 0.6 0.8 1 probability histogram. Coherence

0 (a) (b) Based on the coherence analysis, the coherence of the Gaofen-3 interferometric data of the DengfengFigureFigure 4.area 4.Coherence Coherence was 0.413, analysis analysis and of theof the the standard flattened flattened interferogram deviationinterferogram of withthe with distribution (a ()a) coherence coherence mapwas map and0.186. and (b (b) Partial) coherence coherence regions hadprobability geometricprobability histogram.decorrelation histogram. due to terrain reliefs. The 29 days of time interval also resulted in certain degrees of time decorrelation. InterferogramsInterferogramsBased on the of coherenceof Sentinel-1 Sentinel analysis,-1 of of the the same same the area coherencearea with with six six of days days the and Gaofenand thirty-six thirty-3 interferometric-six temporal temporal baseline baseline data ofwere were the derivedderivedDengfeng to to make makearea comparisonswas comparisons 0.413, and with with the Gaofen-3. Gaofenstandard-3. Figure Figuredeviation5 a,c5a,c showsof shows the thedistribution the interferogram interferogram was 0.186. and and coherence coherencePartial regions map map withwithhad the geometricthe temporal temporal decorrelation baseline baseline of six ofdue six days, to days, terrain and and the reliefs. average the averageThe coherence 29 days coherence of coe timeffi cient coefficientinterval is 0.67. also is Figure resulted0.67. 5 Figureb,d in shows certain 5b,d theshowsdegrees interferogram the of interferogramtime decorrelation. and coherence and coherence map with map the with temporal the temporal baseline ofbaseline thirty-six of thirty days,-six and days, the average and the coherenceaverageInterferograms coherence coefficient coeffic isof 0.23.Sentinelient is- 10.23. of the same area with six days and thirty-six temporal baseline were derivedItIt can can to be makebe seen seen comparisons from from the the Sentinel-1 Sentinel with Gaofen-1 data, data,-3. the the Figure interferograms interferograms 5a,c shows after theafter interferogram six six days days appear appear and to to coherence be be less less noisy noisy map thanthanwith the thethe interferograms interferograms temporal basel derived derivedine of fromsix from days, Gaofen-3. Gaofen and- 3. the However,However, average the the coherence phase phase decoherence decoherence coefficient is is more0.67. more seriousFigure serious 5b for for,d thetheshows interferograms interferograms the interferogram after after thirty-six thirty and coherence-six days. days. Long Longmap temporalwithtemporal the temporal baselinebaseline couldcouldbaseline bebe theofthe thirty mainmain-six sourcesource days, of of and phase phase the noisenoiseaverage due due coherence to to the the change change coeffic of of theient the ground isground 0.23. features. features. It can be seen from the Sentinel-1 data, the interferograms after six days appear to be less noisy than the interferograms derived from Gaofen-3. However, the phase decoherence is more serious for the interferograms after thirty-six days. Long temporal baseline could be the main source of phase noise due to the change of the ground features.

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Figure 5. Cont.

(a) (b) Appl. Sci. 2020, 10, 806 7 of 17 Appl. Sci. 2020, 10, 806 7 of 16

0.8

0.6

0.4

0.2

Figure 5. Interferograms of the Sentinel-1Sentinel-1 satellite data in the Dengfeng area, China:China: (a) interferogram withwith temporal baseline baseline of of six six days, days, (b (b) )interferogram interferogram with with temporal temporal baseline baseline of ofthirty thirty-six-six days, days, (c) (coherencec) coherence map map of ofthe the interferogram interferogram with with temporal temporal baseline baseline of of six six days, days, ( (dd)) coherence coherence map map of thethe interferograminterferogram withwith temporaltemporal baselinebaseline ofof thirty-sixthirty-six days.days.

3.3. Phase Filtering InIn this study,study, thethe multi-scalemulti-scale phase-filteringphase-filtering methodmethod inin thethe waveletwavelet domaindomain waswas usedused toto conduct phasephase filtering filtering on on the the flattened flattened interferogram. interferogram. This This method method was was compared compared with with the theclassical classical Lee and Lee andthe improvedthe improved Goldstein Goldstein filter filter methods methods [21 [,2122,]22 to] to validate validate the the ability ability o off phase phase noise noise removal and and preservepreserve thethe phasephase detailsdetails andand edges.edges. It has been proved to have better filteringfiltering precision and can reducereduce thethe influenceinfluence ofof datadata processingprocessing errorserrors onon thethe evaluationevaluation experiment.experiment. The multi multi-scale-scale phase phase filtering filtering method method based based on on the the wavelet wavelet domain domain is shown is shown in Figure in Figure 6. The6. Thefiltering filtering algorithm algorithm was was based based on on the the analysis analysis of of the the wavelet wavelet residual residual band band using using discrete digital transformations,transformations, which consisted of six steps describeddescribed brieflybriefly below.below. The details are describeddescribed by LLópezópez and FFàbregasàbregas [[2323].]. Appl. Sci. 2020, 10, 806 8 of 17 Appl. Sci. 2020, 10, 806 8 of 16 cos sin     Z Z  

DWPT (3 scales)

Real part Real Imagpart

Sinal mask Adaptive genernation threshold

Mask ×2 ×2

Coherence map Inverse DWT Mask growing decimation (1 scale)

No No i=3? i=3? i=3?

Yes Yes

Phase calculation  X FigureFigure 6. 6. Multi-scaleMulti-scale phase filteringfiltering process in thethe waveletwavelet domain domain via via six six steps. steps. DWPT: discrete discrete wavelet package transform; DWT: discrete wavelet transform; Imag part: imaginary part; φ : original wavelet package transform; DWT: discrete wavelet transform; Imag part: imaginary part;x  x : phase; i: iteration times. original phase; i : iteration times. Step 1: three-level wavelet transform on the complex interferometric phase. In the third decompositionStep 1: three level,-level the wavelet wavelet transformfilter was applied on the to complex all bands interferometric using a discrete phase. wavelet In the package third transformdecomposition (DWPT). level, the wavelet filter was applied to all bands using a discrete wavelet package transforStepm 2: (DWPT). mask generation. The pixel of the mask was determined by the signal quality. StepStep 3:2: Realmask and generation. imaginary The parts. pixel The of the real mask and imaginarywas determined parts ofby the the coe signalfficient quality detected as the signalStep were 3: multipliedReal and imaginary by 2, and parts the noise. The coe realffi andcient imaginary remained parts unchanged. of the coefficient detected as the signalStep were 4: inversemultiplied discrete by 2, waveletand the transformation.noise coefficient Onlyremained one wavelet unchanged. scale was reduced. StepStep 5:4: mask-locatinginverse discrete signal wavelet coe ffitransformationcients. To obtain. Only mask-locating one wavelet signal scale coewasffi reduced.cients for previous scalesStep (higher-frequency 5: mask-locating scales), signala coefficients. new mask wasTo obtain derived mask from-locating that generated signal coefficients in step 2.) for previous scalesWe (higher iterated-frequency steps 3–5 scales), (dashed a new lines mask in Figure was derived6) three from times that to obtain generated the complex in step 2. signal) in the originalWe domain.iterated steps 3–5 (dashed lines in Figure 6) three times to obtain the complex signal in the originalStep domain. 6: Phase calculation. Meanwhile,Step 6: Phase the calculation. classical Lee method [22] and the improved Goldstein method [21] were used to carryMeanwhile, out phase the filtering classical and Lee compared method [22 to] and the wavelet-basedthe improved Goldstein multi-scale me method.thod [21] Figurewere used7 is ato partialcarry out region phase of thefiltering phase and image. compared The original to the interferometricwavelet-based multi phase-scale image method. was annihilated Figure 7 byis a speckle partial noise.region Afterof theprocessing phase image. with The the original three filteringinterferometric methods, phase the SNRimage of was the annihilated phase image by was speckle improved noise. toAfter diff erent processing degrees. with From the thr theee visual filtering effects, methods, the filtering the SNR results of the of phase the Lee image method was kept improved a certain to proportiondifferent degrees. of phase From noise, the whereas visual the effects, Goldstein the filtering method and results our of method the Lee had method almost the kept same a certain effect onproportion noise removal. of phase It wasnoise, visually whereas judged the Goldstein that both themethod Goldstein and our and method our method had almost outperformed the same the effect Lee methodon noise with removal. regards It was to phase visually de-noising judged ability.that both Therefore, the Goldstein the Goldstein and our method method was outperformed mainly used the as aLee comparison method with to our regards method; to thephase Goldstein de-noising method ability. retained Therefore, a small the amount Goldstein of noise method signal, was and mainly some eusedffective as a signal comparison components to our weremethod; lost the to a Goldstein certain extent. method However, retained for a small the phase amount image of noise obtained signal, by and some effective signal components were lost to a certain extent. However, for the phase image obtained by our filtering method, the noise level was slightly lower than that of the Goldstein method, Appl. Sci. 2020, 10, 806 9 of 17 Appl. Sci. 2020, 10, 806 9 of 16 Appl. Sci. 2020, 10, 806 9 of 16 ourand filtering more detai method,lsls andand the edgesedges noise werewere level preserved.preserved. was slightly FigureFigure lower 88 thanshowsshows that thethe of locallocal the Goldstein detailsdetails ofof method, FigureFigure 7.7. and ItIt cancan more bebe detailsobserved and that edges the were Lee preserved.method had Figure some8 abilityshows for the maintaining local details ofphase Figure details7. It can and be stripe observed edges, that but thethethe Lee higherhigher method noisenoise had levellevel some stillstill ability affectedaffected for the maintainingthe extractionextraction phase ofof truetrue details phases.phases. and OnOn stripe tthe edges, other buthand, the the higher Goldstein noise levelmethod still achieved affected thegood extraction results in of general, true phases. but for On some the other regions hand,,, thethe the detailsdetails Goldstein werewere blurredblurred method becausebecause achieved ofof goodthethe filtering,filtering, results in andand general, eveneven butthethe foredgesedges some ofof regions,thethe phasephase the jumpjump details werewere were filteredfiltered blurred withwith because poorpoor ofpreservation,preservation, the filtering, whichwhich and even willwill thehave edges a negative of the phase impact jump on werethe subsequent filtered with unwrapping poor preservation, and the which inversion will haveof terrain a negative or deformation. impact on theOur subsequent method not unwrapping only filtered and out the the inversion phase noise, of terrain but oralso deformation. preserved the Our original method phase not only details filtered and outfringefringe the edgesedges phase moremore noise, completely.completely. but also preserved the original phase details and fringe edges more completely.

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FigureFigure 7. 7.Comparison Comparison of of the the results resultsresults of ofof three threethree phase phasephase ﬁltering ffilteringiltering methods: methodsmethods (a):: original((a)) originaloriginal interferogram, interferogram,interferogram, (b) Lee ((b)) method,Lee method, (c) Goldstein (cc)) Goldstein method, method, and ( andd) our (d)) method. our method..

-π -π ((a)) ((b)) ((c)) ((d)) Figure 8. Cont. Appl. Sci. 2020, 10, 806 10 of 17 Appl.Appl. Sci. 2020Sci. 2020, 10, ,806 10, 806 10 of10 16 of 16

(e) (e) (f) (f) (g) (g) (h) (h)

FigureFigure 8. 8. ComparisonComparison 8. Comparison of of the the of details thedetails details of three-phase of three of three-phase ﬁltering-phase filtering methods filtering methods showing methods showing local showing details: local local details:(a,e ) details: original (a,e ) ( a,e) originalinterferogram,original interferogram, interferogram, (b,f) Lee ( method,b,f )( bLee,f) Leemethod, (c,g )method, Goldstein (c,g )( cGoldstein,g method,) Goldstein method, and method, (d,h )and our and(d method.,h ()d our,h) ourmethod. method.

TheThe multi-scalemulti multi-scale-scale phasephase-filtering phase-filtering-filtering method method in the in the wavelet wavelet domain domain was was applied applied to the to the flattenedflattened flattened interferograminterferogram (Figure(Figure (Figure3 b)3b of) 3bof Gaofen-3.) Gaofenof Gaofen- As3. As shown-3. shownAs shown in Figure in Figurein9 ,Figure the 9, average the 9, theaverage coherence average coherence valuecoherence of value the value filtered of theof the filteredinterferogramfiltered interferogram interferogram is increased is increased is to increased 0.834. to 0.834. to 0.834.

FigureFigure 9. Interferogram Interferogram 9. Interferogram after after after phase phase phase filtering ﬁltering filtering with with with the the the multi multi-scale multi-scale-scale phase phase-ﬁltering phase-filtering-filtering method method method in in the in the waveletwavelet domain.domain domain. .

3.4. 3.4.Phase Phase Phase Unwrapping Unwrapping BasedBased onon the theon results theresults results of of the the of high-accuracy thehigh high-accuracy-accuracy phase phase phase ﬁltering, filtering, filtering, the the minimum theminimum minimum cost cost ﬂow cost flow method flow method wasmethod usedwas was usedto unwrapused to unwrap to theunwrap phase the thephase with phase with a coherence with a coherence a coherence higher higher than higher than 0.3. than The0.3. 0.3.The unwrapping The unwrapping unwrapping result result is result shown is shown is shown in Figure in Figure in Figure10. 10. 10.

(rad)(rad) -20 -20

-40 -40

-60 -60

-80 -80

-100 -100

-120 -120

-140 -140

-160 -160 FigureFigure 10. Phase10. Phase unwrapping unwrapping results results with with the minimumthe minimum cost costflow flow method. method. Appl. Sci. 2020, 10, 806 10 of 16

(e) (f) (g) (h)

Figure 8. Comparison of the details of three-phase filtering methods showing local details: (a,e) original interferogram, (b,f) Lee method, (c,g) Goldstein method, and (d,h) our method.

The multi-scale phase-filtering method in the wavelet domain was applied to the flattened interferogram (Figure 3b) of Gaofen-3. As shown in Figure 9, the average coherence value of the filtered interferogram is increased to 0.834.

Figure 9. Interferogram after phase filtering with the multi-scale phase-filtering method in the wavelet domain.

3.4. Phase Unwrapping

Appl. Sci.Based2020 ,on10, 806the results of the high-accuracy phase filtering, the minimum cost flow method11 ofwas 17 used to unwrap the phase with a coherence higher than 0.3. The unwrapping result is shown in Figure 10.

(rad) -20

-40

-60

-80

-100

-120

-140

-160

FigureFigure 10.10. PhasePhase unwrappingunwrapping resultsresults withwith thethe minimumminimum costcost ﬂowflow method.method.

3.5. Baseline Measurement and Elevation Maps In our study, the vertical baseline of the interferometric pair was 598 m, the slant range was 990 km, the viewing angle was 43.36◦, and the radar operating wavelength of Gaofen-3 was 5.55 cm. Therefore, the elevation ambiguity was as follows:

λR sin θ ∆h = 1 32.0m. (1) 2π 2B ' ⊥ The elevation was obtained in two ways. The first involved estimating the baseline using orbital parameters, and using the residual fringe frequency of the flattened interferogram to estimate the trend system error of the baseline parameters and refining the baseline. The second was to obtain high-accuracy baseline parameters through the ground control points and phase unwrapping and then solving the elevation. These two methods were consequently employed to measure the evaluation accuracy of InSAR technology for Gaofen-3 data with or without ground control points. Figure 11a,b shows the obtained vertical baselines with or without ground control points, respectively. As can be seen from Figure 11a, the time variation of the vertical baseline was small due to the high degree of parallelism of the orbits. Limited by the orbital accuracy, the vertical baselines calculated by the orbital parameters could not reflect the small number of changes in azimuth. Therefore, the flat-earth phase calculated by this baseline will bring about systematic errors, and the residual regular fringes will be shown in the flattened interferogram. After estimating the phase residuals by the means of least squares adjustment and refining the baseline, the obtained vertical baseline was compared to that calculated by the ground control points, as shown in Figure 11b. There existed a nearly linear difference in the baseline parameters obtained by the orbital parameters combined with the means of least squares adjustment and ground control points, ranging from 0 to 4 cm, mainly varying in azimuth, with small changes in range. The system difference was due to the limited accuracy of the estimation of the baseline parameters using residual phase frequency, which will finally be reflected by the accuracy differences in evaluation calculated with and without ground control points. The DEM calculated and inverted with control points is shown in Figure 12a, and the DEM with baselines through the orbital parameters, refined and inverted, is shown in Figure 12b. Appl. Sci. 2020, 10, 806 11 of 16

3.5. Baseline Measurement and Elevation Maps In our study, the vertical baseline of the interferometric pair was 598 m, the slant range was 990 km, the viewing angle was 43.36°, and the radar operating wavelength of Gaofen-3 was 5.55 cm. Therefore, the elevation ambiguity was as follows: R sin 1 . hm2 32.0 (1) 2B

The elevation was obtained in two ways. The first involved estimating the baseline using orbital parameters, and using the residual fringe frequency of the flattened interferogram to estimate the trend system error of the baseline parameters and refining the baseline. The second was to obtain high-accuracy baseline parameters through the ground control points and phase unwrapping and then solving the elevation. These two methods were consequently employed to measure the evaluation accuracy of InSAR technology for Gaofen-3 data with or without ground control points. Figure 11a,b shows the obtained vertical baselines with or without ground control points, respectively. As can be seen from Figure 11a, the time variation of the vertical baseline was small due to the high degree of parallelism of the orbits. Limited by the orbital accuracy, the vertical baselines calculated by the orbital parameters could not reflect the small number of changes in azimuth. Therefore, the flat-earth phase calculated by this baseline will bring about systematic errors, and the residual regular fringes will be shown in the flattened interferogram. After estimating the phase residuals by the means of least squares adjustment and refining the baseline, the obtained vertical baseline was compared to that calculated by the ground control points, as shown in Figure 11b. There existed a nearly linear difference in the baseline parameters obtained by the orbital parameters combined with the means of least squares adjustment and ground control points, ranging from 0 to 4 cm, mainly varying in azimuth, with small changes in range. The system difference was due to the limited accuracy of the estimation of the baseline parameters using residual phase frequency, which will finally be reflected by the accuracy differences in evaluation calculated with and without ground control points. The DEM calculated and inverted with control points is shown in Figure 12a, and the DEM with baselines through the orbital parameters, refined and inverted, is shown in Figure 12b. Appl. Sci. 2020, 10, 806 12 of 17

(m) 605 604 603 602 601 600 599 598 Appl. Sci. 2020, 10, 806 12 of 16 Appl. Sci. 2020, 10, 806 597 12 of 16

(a)

(m) (m)0.05 0.05 0.04 0.04 0.03 0.03 0.02 0.02 0.01 0.01 0.00 0.00 -0.01 -0.01 -0.02 -0.02 -0.03 -0.03 -0.04 -0.04 -0.05 -0.05 (b) (b)

Figure 11. Distribution of vertical baselines (B) obtained by ground control points (a) and the FigureFigure 11. 11. DistributionDistribution of verticalvertical baselines baselines (B) (B) obtained obtained by groundby ground control control points points (a) and ( thea) and diﬀerence the difference distribution of vertical baselines (ΔB) obtained through ground control points, orbits, and differencedistribution distribution of vertical of baselines vertical (baselines∆B) obtained (ΔB) throughobtained ground through control ground points, control orbits, points and, least-squaresorbits, and least-squares adjustment method (b). leastadjustment-squares methodadjustment (b). method (b). 0 100 200 300 400 500 600 700 800 900 1000 0 100 200 300 400 500 600 700 800 900 1000 (m) (m)

(a) (b) (a) (b) FigureFigure 12.12. TheThe digitaldigital elevationelevation modelsmodels acquiredacquired withwith groundground controlcontrol pointspoints (GCPs)(GCPs) ((aa)) andand withoutwithout Figure 12. The digital elevation models acquired with ground control points (GCPs) (a) and without GCPsGCPs ((bb).). GCPs (b). 4. Discussion 4. Discussion 4.1. Elevation Accuracy Evaluation with GCPs and 1:2000-Scale DEM Data 4.1. Elevation Accuracy Evaluation with GCPs and 1:2000-Scale DEM Data Six GCPs (Ground Control Points) in the Dengfeng area were adopted to evaluate the height Six GCPs (Ground Control Points) in the Dengfeng area were adopted to evaluate the height accuracy derived from the Gaofen-3 SAR images. Those GCPs were obtained from six corner accuracy derived from the Gaofen-3 SAR images. Those GCPs were obtained from six corner reflectors, which were placed in the study area in advance. Figure 13 shows the distribution of six reflectors, which were placed in the study area in advance. Figure 13 shows the distribution of six corner reflector control points on Gaofen-3 SAR image. corner reflector control points on Gaofen-3 SAR image. Appl. Sci. 2020, 10, 806 13 of 17

4. Discussion

4.1. Elevation Accuracy Evaluation with GCPs and 1:2000-Scale DEM Data Six GCPs (Ground Control Points) in the Dengfeng area were adopted to evaluate the height accuracy derived from the Gaofen-3 SAR images. Those GCPs were obtained from six corner reﬂectors, which were placed in the study area in advance. Figure 13 shows the distribution of six corner reﬂector

Appl.control Sci. points2020, 10, on806Gaofen-3 SAR image. 13 of 16

103 101 102 104

105

106

Figure 13. DistributionDistribution of corner reflector reﬂector control points on Gaofen Gaofen-3-3 SAR image.image.

The elevation value obtainedTable 4. Elevation by Gaofen-3 accuracy was evaluated compared by withsix GCPs six. GCPs, as shown in Table4.

The difference is from 1.694Elevation to 6.494 Value m and theElevation root mean Value square error is about 3.3 m. No. − Difference (m) from GCPs (m) from Gaofen-3 SAR Images (m) 101 Table470.257 4. Elevation accuracy468.563 evaluated by six GCPs.−1.694 102 425.266 423.952 −1.314 Elevation Value Elevation Value from Gaofen-3 No. 103 406.783 413.267 6.484Di fference (m) 104from GCPs482.970 (m) SAR485.012 Images (m) 2.042 101105 470.257364.747 368.449 468.563 3.702 1.694 − 102106 425.266372.336 378.83 423.952 6.494 1.314 − 103 406.783 RMS 413.267E 3.305 6.484 104 482.970 485.012 2.042 The elevation105 value 364.747 obtained by Gaofen-3 was 368.449 compared with six GCPs, as 3.702 shown in Table 4. The difference106 is from −1.694 372.336 to 6.494 m and the root 378.83 mean square error is about 3.3 6.494 m. In order to carry out more accurate verificationRMSE work, 1:2000-scale DEM data 3.305 obtained from an aerial survey. The visual difference between the two DEMs in Figure 12 is not significant, and the difference between the obtained DEM and the 1:2000-scale DEM was therefore calculated to measure In order to carry out more accurate verification work, 1:2000-scale DEM data obtained from an the elevation accuracy (Figure 14a,b). According to the statistics of elevation residuals, the height aerial survey. The visual difference between the two DEMs in Figure 12 is not significant, and the measurement accuracy of InSAR technology for the Gaofen-3 satellite data in the Dengfeng area was difference between the obtained DEM and the 1:2000-scale DEM was therefore calculated to measure better than 4 m under control conditions, whereas the height measurement accuracy of InSAR under the elevation accuracy (Figure 14a,b). According to the statistics of elevation residuals, the height non-control conditions was better than 7 m. measurement accuracy of InSAR technology for the Gaofen-3 satellite data in the Dengfeng area was better than 4 m under control-10 -8 conditions,-6 -4 whereas-2 0 the2 height4 measurement6 8 10 accuracy of InSAR under non-control conditions was better than 7 m. (m)

(a) (b)

Figure 14. Distribution maps of evaluation difference between the 1:2000-scale DEM and the obtained DEM with GCPs (a) and without GCPs (b). Appl. Sci. 2020, 10, 806 13 of 16

103 101 102 104

105

106

Figure 13. Distribution of corner reflector control points on Gaofen-3 SAR image.

Table 4. Elevation accuracy evaluated by six GCPs.

Elevation Value Elevation Value No. Difference (m) from GCPs (m) from Gaofen-3 SAR Images (m) 101 470.257 468.563 −1.694 102 425.266 423.952 −1.314 103 406.783 413.267 6.484 104 482.970 485.012 2.042 105 364.747 368.449 3.702 106 372.336 378.83 6.494 RMSE 3.305

The elevation value obtained by Gaofen-3 was compared with six GCPs, as shown in Table 4. The difference is from −1.694 to 6.494 m and the root mean square error is about 3.3 m. In order to carry out more accurate verification work, 1:2000-scale DEM data obtained from an aerial survey. The visual difference between the two DEMs in Figure 12 is not significant, and the difference between the obtained DEM and the 1:2000-scale DEM was therefore calculated to measure the elevation accuracy (Figure 14a,b). According to the statistics of elevation residuals, the height measurement accuracy of InSAR technology for the Gaofen-3 satellite data in the Dengfeng area was betterAppl. Sci. than2020 4, 10 m, 806under control conditions, whereas the height measurement accuracy of InSAR u14nder of 17 non-control conditions was better than 7 m.

-10 -8 -6 -4 -2 0 2 4 6 8 10

(m)

(a) (b)

Figure 14. DistributionDistribution maps maps of of evaluation evaluation difference diﬀerence between between the 1:2000 1:2000-scale-scale DEM and and the the obtained obtained DEM with GCPs ( a) and without GCPs ( b).

Table 5. Yaogan-29 satellite data of the experimental area (Dengfeng area, China).

Master Image Slave Image Date 20 December 2015 18 November 2016 Mode Strip Resolution 1 m Wave length 3.1 cm (X band) Path direction Descending

In this case, the vertical baseline of the interferometric pair was 1935.54 m, the slant range was 755 km, the viewing angle was 34.91◦, and the radar operating wavelength was 3.1 cm. The long vertical baseline means a very small ambiguity as follows:

λR sin θ ∆h = 1 3.48m. (2) 2π 2B ' ⊥ The DEM calculated with control points is shown in Figure 15a. We further calculated the diﬀerence between the obtained DEM and 1:2000-scale DEM to measure the elevation accuracy (Figure 15b). Appl. Sci. 2020, 10, 806 14 of 16

4.2. Comparison with Yaogan-29 Satellite For comparison with other SAR sensors and further analysis of the interferometric measurement capability of Chinese SAR satellites, we processed the dataset of the same area acquired by the Chinese non-civilian Yaogan-29 satellite with the same methods. The basic information of the SAR pair acquired by Yaogan-29 sensors is shown in Table 5. In this case, the vertical baseline of the interferometric pair was 1935.54 m, the slant range was 755 km, the viewing angle was 34.91°, and the radar operating wavelength was 3.1 cm. The long vertical baseline means a very small ambiguity as follows: R sin 1 . hm2 3.48 (2) 2B

Table 5. Yaogan-29 satellite data of the experimental area (Dengfeng area, China).

Master Image Slave Image Date 20 December 2015 18 November 2016 Mode Strip Resolution 1 m Wave length 3.1 cm (X band) Path direction Descending

The DEM calculated with control points is shown in Figure 15a. We further calculated the difference between the obtained DEM and 1:2000-scale DEM to measure the elevation accuracy (Figure 15b). When ignoring the absence of local elevation, the elevation accuracy of the DEM in Dengfeng acquired through Yaogan-29 reached an accuracy of better than 2 m on level ground and better than 5 m in mountainous areas. In this case, benefiting from the large vertical baseline, the elevation accuracy of Yaogan-29 was better than that of Gaofen-3. However, the geometrical decorrelation of theAppl. former Sci. 2020 ,is10 more, 806 severe than that of the latter. Figure 16 shows the comparison of the effective15 of 17 elevation distributions obtained by these two sensors.

0 100 200 300 400 500 600 700 800 900 1000 -10 -8 -6 -4 -2 0 2 4 6 8 10 (m) (m)

(a) (b)

FigureFigure 15. 15. DEMDEM acquired acquired by by Yaogan-29 Yaogan- sensor29 sensor with withGCPs GCPs (a) and (a the) and distribution the distribution map of the map evaluation of the evaluationdifference betweendifference the between 1:2000-scale the 1:2000 DEM-scale and the DEM obtained and the DEM obtained (b). DEM (b). When ignoring the absence of local elevation, the elevation accuracy of the DEM in Dengfeng acquired through Yaogan-29 reached an accuracy of better than 2 m on level ground and better than 5 m in mountainous areas. In this case, benefiting from the large vertical baseline, the elevation accuracy of Yaogan-29 was better than that of Gaofen-3. However, the geometrical decorrelation of the former is more severe than that of the latter. Figure 16 shows the comparison of the effective elevation distributionsAppl. Sci. 2020, 10 obtained, 806 by these two sensors. 15 of 16

(a) (b)

Figure 16. The effective eﬀective elevation distributions obtained by Yaogan-29Yaogan-29 ((aa)) andand Gaofen-3Gaofen-3 ((bb).).

5. Conclusions The Gaofen-3Gaofen-3 satellite is China’s ﬁrstfirst SAR satellite to have interferometric abilities. In In this this study, study, interferometricinterferometric datadata processing processing and and height height measurement measurement experiments experiments were were carried carried out. out. Data Data from from the Dengfengthe Dengfeng area area was was used used to evaluate to evaluate the interferometric the interferometric imaging imaging and measurement and measurement capabilities capabilities of the domesticof the domestic SAR satellite. SAR satellite. In order In to order reduce to thereduce errors the introduced errors introduced by the processing by the processing as much as possible,much as apossible, high-accuracy a high- InSARaccuracy image InSAR co-registration image co-registration strategy was strategy adopted. was adopted.The co-registration The co-registration accuracy reachedaccuracy the reached 0.01-pixel the 0.01 level.-pixel Moreover, level. Moreover, a multi-scale a multi phase-scale ﬁltering phase methodfiltering inmethod the wavelet in the domainwavelet wasdomain employed was employed to improve to improve the SNR the of SNR the interferometric of the interferometric phase. phase. The interferometry The interferometry baselines baselines were subsequentlywere subsequently calculated calculated with and with without and without ground groundcontrol point control schemes, point schemes, respectively. respectively. The elevation The inversionelevation was inversion performed was to performed evaluate the to accuracy evaluate of the the interferometric accuracy of the height interf measurementerometric height under measurement under these conditions. Initially, our experimental results showed that the height measurement accuracy of Gaofen-3 repeated-orbit interferometry under control conditions was higher than 4 m and higher than 7 m under non-control conditions. Our experiments proved that China’s SAR satellite Gaofen-3 has a certain degree of interferometric imaging and measurement capabilities. It has a high coherence and SNR in the presence of certain time and geometrical decorrelation, which indicates its mapping abilities and constitutes a major breakthrough in the field of Chinese SAR technology. Since Gaofen-3 is a single-antenna SAR satellite, with a repetitive orbit interferometry mode, it would be more useful when applied to the field of surface deformation monitoring. However, the capability of specific deformation monitoring needs further verification. In this study, the interferometry verification and evaluation of Gaofen-3 satellite data with a fine strip I model was carried out. Other imaging modes, including all-polarization and bunching modes, should be verified, and the applications and evaluations related to surface deformation monitoring should be studied in the near future.

Author Contributions: data curation, Z.C.; formal analysis, Y.Z.; investigation, Y.Z.; methodology, Y.Z. and Z.C.; project administration, G.Z.; supervision, G.Z.; validation, Y.Z.; visualization, Z.C.; writing—original draft, Z.C. All authors have read and agreed to the published version of the manuscript.

Funding: This research was funded by Wuhan university (Wuhan, China) through National Key R&D Program of China (2018YFC0825803), Natural Science Foundation of China projects (NSFC) (No: 41801397).

Acknowledgments: The authors would like to thank the editors and the anonymous reviewers for their constructive suggestions.

Conflicts of Interest: The authors declare no conflict of interest.

Appl. Sci. 2020, 10, 806 16 of 17 these conditions. Initially, our experimental results showed that the height measurement accuracy of Gaofen-3 repeated-orbit interferometry under control conditions was higher than 4 m and higher than 7 m under non-control conditions. Our experiments proved that China’s SAR satellite Gaofen-3 has a certain degree of interferometric imaging and measurement capabilities. It has a high coherence and SNR in the presence of certain time and geometrical decorrelation, which indicates its mapping abilities and constitutes a major breakthrough in the field of Chinese SAR technology. Since Gaofen-3 is a single-antenna SAR satellite, with a repetitive orbit interferometry mode, it would be more useful when applied to the field of surface deformation monitoring. However, the capability of specific deformation monitoring needs further verification. In this study, the interferometry verification and evaluation of Gaofen-3 satellite data with a fine strip I model was carried out. Other imaging modes, including all-polarization and bunching modes, should be verified, and the applications and evaluations related to surface deformation monitoring should be studied in the near future.

Author Contributions: Data curation, Z.C.; formal analysis, Y.Z.; investigation, Y.Z.; methodology, Y.Z. and Z.C.; project administration, G.Z.; supervision, G.Z.; validation, Y.Z.; visualization, Z.C.; writing—original draft, Z.C. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by Wuhan university (Wuhan, China) through National Key R&D Program of China (2018YFC0825803), Natural Science Foundation of China projects (NSFC) (No: 41801397). Acknowledgments: The authors would like to thank the editors and the anonymous reviewers for their constructive suggestions. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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