Evaluation of Applicability of Various Color Space Techniques of UAV Images for Evaluating Cool Roof Performance

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Article Evaluation of Applicability of Various Color Space Techniques of UAV Images for Evaluating Cool Roof Performance

Kirim Lee 1 , Jihoon Seong 1, Youkyung Han 2 and Won Hee Lee 2,* 1 Department of Spatial Information, Kyungpook National University, Daegu 41566, Korea; [email protected] (K.L.); [email protected] (J.S.) 2 School of Convergence and Fusion System Engineering, Kyungpook National University, Sangju 37224, Korea; [email protected] * Correspondence: [email protected]; Tel.: +82-054-530-1492

Received: 13 July 2020; Accepted: 13 August 2020; Published: 14 August 2020

Abstract: Global warming is intensifying worldwide, and urban heat islands are occurring as urbanization progresses. The cool roof method is one alternative for reducing the urban heat island phenomenon and lowering the heat on building roofs for a comfortable indoor environment. In this study, a cool roof evaluation was performed using an unmanned aerial vehicle (UAV) and a red, green and blue (RGB) camera instead of a laser thermometer and a thermal infrared sensor to evaluate existing cool roofs. When using a UAV, an RGB sensor is used instead of expensive infrared sensor. Various color space techniques, namely light-reﬂectance value, hue saturation value (HSV), hue saturation lightness, and YUV (luma component (Y) and two chrominance components, called U (blue projection) and V (red projection)) derived from RGB images, are applied to evaluate color space techniques suitable for cool roof evaluation. This case study shows the following quantitative results: among various color space techniques investigated herein, the white roof with lowest temperature (average surface temperature: 44.1 ◦C; average indoor temperature: 33.3 ◦C) showed highest HSV, while the black roof with the highest temperature (surface temperature average: 73.4 ◦C; indoor temperature average: 37.1 ◦C) depicted the lowest HSV. In addition, the HSV showed the highest correlation in both the Pearson correlation coeﬃcient and the linear regression analyses when the correlation among the brightness, surface temperature, and indoor temperature of the four color space techniques was analyzed. This study is considered a valuable reference for using RGB cameras and HSV color space techniques, instead of expensive thermal infrared cameras, when evaluating cool roof performance.

Keywords: UAV; cool roof; thermal images; color space techniques; surface temperature; indoor temperature

1. Introduction Temperature rises are intensifying with global warming worldwide. Abnormally high temperatures have occurred in Central Africa, Europe, the Middle East, Alaska, and South America [1,2]. Moreover, with the development of industries, the urban heat island phenomenon has occurred with urbanization, population increase, and vegetation reduction. The phenomenon is mainly caused by asphalt roads, concrete artificial structures, and high-rise buildings. The solar radiation reflected on building roofs raises the external surface temperature by up to 50 to 60 ◦C[3,4]. Such a structure has a bad effect on the living environment of people because it raises the temperature of the city, thereby causing problems for residential life and the cooling load [5]. In an effort to reduce the urban heat island phenomenon, research is being conducted to lower the surface temperature of building roofs.

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The building roof temperature is important because the building roof comprises approximately 20% to 25% of the city’s surface, and the energy consumption for cooling the city building is higher than that of non-urban buildings [6,7]. Cool roofs, rooftop greening, sprinkling treatment, solar power generation, and dual roofs, among others, are being investigated to reduce roof temperature [8–12]. Cool roofs are difficult to install because of structural problems and high installation and maintenance costs. White or light-colored paints that reflect well are applied to cool roofs using heat absorption from the color difference to reduce the heat accumulation in the roof. This can be easily applied to an existing building and is excellent in terms of cost because it is easy to install after the initial design, construction, and completion of the building [13,14]. Two previous studies on cool roof evaluation have been reported. First, evaluations according to environmental conditions, such as the material’s exterior wall thickness, thickness, roof condition, window insulation and size, and solar radiation energy, have been performed [15–19]. Second, only the roof surface temperature was evaluated; the external factors were excluded [20,21]. Satellite images, handle-type thermal infrared (TIR) images, and laser thermometers are frequently used in the existing method of roof surface temperature evaluation. Moreover, studies using unmanned aerial vehicle (UAV) infrared cameras have recently been conducted [22,23]. However, satellite images have a low spatial resolution; hence, the analysis of large areas is possible, and the local analysis is difficult. The handle-type TIR image and the laser thermometer have a disadvantage, in that obtaining the overall temperature of the rooftop surface is difficult, and the TIR camera for a UAV is expensive. To solve these shortcomings, a high spatial resolution is obtained, and the overall image acquisition of the roof surface is realized herein. The cool roof evaluation will be conducted using the RGB camera for a UAV instead of the TIR camera. Many studies have been conducted on the possibility of applying near-field monitoring because of the advantage of the UAV photogrammetry technique being able to obtain high-resolution images [24–26]. RGB cameras are cheaper than TIR cameras and can fly without restrictions in legal spaces, except for military areas; hence, you can obtain high-resolution images in cm and acquire the entire roof image. After applying various color space techniques to the image acquired by the RGB camera, we obtain the correlation between the roof surface temperature value of the TIR image acquired by a thermal infrared camera and the indoor temperature value obtained by a digital thermometer. The applied color space technologies are light-reflectance value (LRV), hue saturation value (HSV), hue saturation lightness (HSL), and YUV (luma component (Y) and two chrominance components, called U (blue projection) and V (red projection)). We evaluate the applicability of the cool roof evaluation by the color space technique through each correlation.

2. Materials and Methods

2.1. Study Area and Equipment The selected study area was the 9th building of Kyungpook National University Sangju Campus in Sangju, Gyeongsangbuk-do, Republic of Korea (Figure1). The colors used to evaluate the cool roof performance are as follows: (1) white, which is the most eﬀective color in cool roof studies; (2) gray, which is similar to cement color; (3) green, which is most commonly used; (4) blue, which is used most often in factories; and (5) black, which absorbs the most sunlight. The gray color represents the color of the initial building. However, in the absence of maintenance, it is changed to dark gray or black; hence, black is added. Blue and green are waterproof paint colors mainly used only in South Korea. Overall, ﬁve colors were applied (Figure1). Energies 2020, 13, 4213 3 of 12 Energies 2020, 13, x FOR PEER REVIEW 3 of 12 Energies 2020, 13, x FOR PEER REVIEW 3 of 12

Figure 1. Study area (2559, Gyeongsang-daero, Sangju-si, Gyeongsangbuk-do, Republic of Korea). FigureFigure 1. StudyStudy area area (2559, (2559, Gyeongsang-daero, Gyeongsang-daero, Sangju-s Sangju-si,i, Gyeongsangbuk-do, Gyeongsangbuk-do, Republic Republic of of Korea). Korea). The red border represents the color for the cool roof performance evaluation. The five colors are The red border represents the color for the cool roofroof performanceperformance evaluation.evaluation. The The five ﬁve colors colors are are presented as capital letters: A: black; B: blue; C: gray; D: green; and E: white. presented as capital letters: A: black; B: blue; C: gray; D: green; and E: white.

Building No. 9 was chosen because the interior space of the fourth ﬂoor is divided equally, and Building No. 9 was chosen because the interior space of the fourth floor is divided equally, and the room indicated by the circle in the ﬁgure is not currently used. Hence, the experiment can be the room indicated by the circle in the figure is notnot currentlycurrently used.used. Hence,Hence, thethe experimentexperiment cancan bebe conducted under the same conditions (Figure2). conducted under the same conditions (Figure 2).

Figure 2. Floor plan of the study site. Figure 2. FloorFloor plan plan of of the the study study site. site. Building No. 9 has a few residents, and a few factors affect the surface temperature because no BuildingBuilding No. No. 9 9 has has a a few few residents, residents, and and a a few few fa factorsctors affect aﬀect the surface temperature because no equipment is used for the experiment rooms, except for the outdoor unit on the rooftop. Figure 3 equipmentequipment is is used for the experiment rooms, except for the outdoor unit on the rooftop. Figure Figure 33 shows a room with indoor temperature measurements. showsshows a a room room with indoor temperature measurements.

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Figure 3. Fourth floor ﬂoor of building No. 9 used for the temperature measurement.

The UAV images were were acquired acquired herein herein using using Inspire Inspire 1, 1,which which can can fly fly for for up upto 18 to min 18 min using using the theremote remote sensing sensing technique, technique, while while the the RGB RGB images images for for applying applying the the color color space space techniques were acquired using a Zenmuse X3 camera. A Zenmuse XT630 TIR camera was used to measure the roof acquired using a ZenmuseFigure X3 3. Figurecame Fourth 3. ra.floor Fourth Aof buildingfloorZenmuse of building No. 9 used XT630 No. for 9 usedthe temperatureTIR for the camera temperature measurement was measurement .used .to measure the roof Figure 3. Fourth floor of buildingFigure 3.No. Fourth 9 used floor for ofthe building temperature No. 9 measurement used for the temperature. measurement. surface temperature for comparing and evaluating the color space technique. 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Furthermore,Furthermore, the temperature the temperature accuracy accuracy was high was at high±5%. atThe ±5%. temperatur The temperature displaye displayunit of theunit digital of the digital temperaturethermometerFurthermore, accuracy was the 0.1 temperature °C, wasFurthermore, and0.3 accuracy its◦ temperatureC. the was temperature high at ±5%. accuracy The temperatur was washigh eat ±0.3 display ±5%. °CThe unit temperatur of the digitale display unit of the digital thermometerthermometer was± 0.1 was°C, and0.1 ° itsC, temperatureand its temperature accuracy accuracy was ±0.3 was °C ±0.3 °C thermometer was 0.1 °Cthermometer, and its temperature was 0.1 °accuracyC, and its was temperature ±0.3 °C accuracy was ±0.3 °C

TableTable 1. UnmannedTable 1. TableUnmanned aerial 1. Unmanned vehicle aerial vehicle aerial(UAV (UAV), ( vehicleUAV), ) camera, camera(UAV), andcamera and digital digitaland thermometer digital thermometer thermometer specifications. specifications. speciﬁcations.specifications. Table 1. Unmanned aerial Tablevehicle 1. (UnmannedUAV), camera aerial and vehicle digital ( UAVthermometer), camera specifications. and digital thermometer specifications. UAV UAV RGB CameraRGB Camera Thermal ThermalInfrared CameraInfrared CameraDigital ThermometerDigital Thermometer UAV RGBUAV Camera RGB CameraThermalRGB Camera Infrared Camera Thermal Thermal InfraredDigital Infrared CameraThermometer Camera DigitalDigital Thermometer UAV RGB Camera Thermal Infrared Camera Digital Thermometer

(Inspire 1) (Inspire 1)(Inspire 1) (Zenmuse X3) (Zenmuse XT630) (Xiaomi) (Inspire 1) (Inspire 1) (Zenmuse X3) (Zenmuse(Zenmuse XT630) XT630) (Zenmuse X3) (Zenmuse(Zenmuse X3) XT630) (Zenmuse XT630) (Xiaomi) (Xiaomi) (Zenmuse X3) (Xiaomi) (Xiaomi) Weight 2935 g Resolution 40004000 × 30004000 × Resolution 640 512 (Inspire 1) Weight Weight2935 g(Zenmuse 2935Resolution g4000 ×X3)Resolution × Resolution4000 ×Resolution (Zenmuse 640 × 512XT630) 640 × 512× Temperature(Xiaomi) Weight 2935 g ResolutionWeight 2935 g ResolutionResolution3000 3000µ 640 × 512 Resolution 640µ × 512 Flight altitude Max: 4500 m Pixel size3000 1.561 1.561 m3000 Pixel size 17 m 0.1 ◦C Flight FlightMax: Max: 1.561× × 1.561 × display unit Flight Max: Flight Pixel1.561Max: size × Pixel 4000 size × Pixel1.561 size ×Pixel size 17 μm 17 μm WeightFlight time2935altitude Max: g 18 altitude minResolutionPixel4500 sizem 4500 FOV m 1.561PixelPixel 94 sizeμsizem◦ 1.561 Resolution μm 17 μmFOV Pixel size 640 45 ×◦ 51217Temperature37 μ ◦m Temperature altitude 4500 m altitude 1.5614500 μ mm 3000 1.561 μm Temperature× Temperature0.1 °C 0.1 °C Speed Max: 22 m/sMax: 18 Focal Max: length 18 3.61 mm Focal length 13 mmdisplay0.1 ° Cunit display unit 0.1 °C FlightMax: time 18Flight time Max:FOV 18 FOV 94° 94° FOV FOV 45° ×display 37° 45° unit× 37° display unit Flight Flight time Max: FlightFOVmin time min 94° 1.561 FOV×FOV 94°45° × 37° FOV 45° × 37° min min 25 C~+135 C PixelMax: 22 size Max: 22 PixelFocal size Focal − 17 ◦μm ◦ altitudeMaximum wind4500Max:Speed m 22 Speed FocalMax: length Focal1.56122 length 3.61μFocalm mm 3.61 mm Focal13 mm (High13 mm gain) TemperatureTemperature Speed 10 m/sFocalm/sSpeed length F-stopm/s3.61 mm Focal Flength/2.8 length3.61 13mm Scene mm length range 13 mm 0.3 C resistance m/s m/s length length 40 C~+550 C accuracy 0.1◦ °C Max: 18 −25 °C~+135−25 °°CC ◦~+135 °C ◦ display unit ± MaximumMaximum −25 °C~+135 °C − −25 °C~+135 °C Flight timeMaximum MaximumFOV 94° Scene FOV Scene (High gain) 45°(High (Low × gain)37° gain)Temperature Temperature minwind wind10 m/s 10 m/sF- stop F-stopScene F/2.8 F/2.8 (High gain) Scene Temperature(High gain) Temperature±0.3 °C ±0.3 °C wind 10 m/s F-windstop 10F/2.8 m/s F-stop rangeF/2.8 range− 40 °C~+550−40 °°CC ~+550 °Caccuracy±0.3 °C accuracy ±0.3 °C resistanceresistance range −40 °C~+550 °Crange accuracy−40 °C~+550 °C accuracy resistanceMax: 22 resistance Focal (Low gain)(Low gain) Speed Focal length 3.61 mm (Low gain) 13 mm(Low gain) 2.2. Unmanned Aerialm/s Vehicle (UAV) Data Acquisitionlength 2.2. Unmanned2.2. Unmanned Aerial Vehicle Aerial (VehicleUAV) Data(UAV Acquisition) Data Acquisition 2.2. Unmanned Aerial Vehicle2.2. Unmanned (UAV) Data Aerial Acquisition Vehicle (UAV) Data Acquisition −25 °C~+135 °C Maximum The UAV imagesThe UAV wereThe images UAV taken imageswere during taken were during taken the third duringthe third weektheScene week third ofof week August August of August 2018(High 2018 when 2018gain) whenthe when temperature the the Temperaturetemperature temperature was was was the wind The UAV10 m/simages were F-stoptakenThe UAV during images the F/2.8thirdwere weektaken of during August the 2018 third when week the of temperatureAugust 2018 waswhen the temperature was±0.3 °C highest and betweenthe highestthe 12:00 highestthe and and highest between and 13:00 between and 12:00 when between and 12:00 13:00the 12:00 and Sun when 13:00 and was range 13:00 the when Sun the when the washighest Sun the the − wasSun40 highest °C~+550 [ thewas27 , highest28 the[27 ]., °C28]. highest Moreover, [27Moreover,,28]. [27 accuracy,Moreover,28]. theMoreover, the photographs the the resistancethe highest and between 12:00 and 13:00 when the Sun was the highest [27,28]. Moreover, the photographsphotographsphotographs were taken were wereon taken a cleartaken on a day on clear a without clear day withoutday clouds without clouds(exte cloudsrnal (exte average rnal(exte averagernal temperature: average temperature: temperature: 31.1 °C 31.1; ° 31.1C; °C; were takenphotographs on a clear were taken day onwithout a clear day clouds without (external clouds (exte averagernal average temperature: temperature:(Low gain) 31.1 31.1 °C; ◦C; relative humidity:

60.4%; and wind speed: 0.69 m/s) to avoid the influence of shadows. Pix4D Capture, a UAV flight planning2.2. Unmanned software Aerial linked Vehicle to Google(UAV) Data Earth Acquisition and OpenStreetMap, was used to maintain a constant flight altitudeThe of UAV 70 m, images overlapping were taken 70% along during the the automatic third w flighteek of route August during 2018 the when data the acquisition temperature (Figure was4). Thethe UAVhighest flew and at thebetween lowest 12:00 speed and of 3 m13:00/s to when avoid thethe cameraSun was eff ects.the Ahighest total of[27,28]. 149 UAV Moreover, images were the acquired,photographs and were an orthophoto taken on wasa clear made day using without Agisoft’s clouds Photoscan. (external average temperature: 31.1 °C; relative humidity: 60.4%; and wind speed: 0.69 m/s) to avoid the influence of shadows. Pix4D Capture, a UAV flight planning software linked to Google Earth and OpenStreetMap, was used to maintain a

Energies 2020, 13, x FOR PEER REVIEW 5 of 12 constant flight altitude of 70 m, overlapping 70% along the automatic flight route during the data Energiesacquisition2020, 13(Figure, 4213 4). The UAV flew at the lowest speed of 3 m/s to avoid the camera effects. A5 total of 12 of 149 UAV images were acquired, and an orthophoto was made using Agisoft’s Photoscan.

Figure 4.4. FlightFlight course course over over the the study study area. area. The grayThe circlesgray indicatecircles indicate the location the oflocation the image of acquisition.the image acquisition. Lens distortion correction was performed before image matching using Agisoft’s Photoscan softwareLens [29distortion,30]. The correction model used was for performed the lens distortion before image correction matching was usin Brown’sg Agisoft’s distortion Photoscan model, whichsoftware was [29,30]. calculated The asmodel follows used (Equations for the lens (1) todistortion (6)): correction was Brown’s distortion model, which was calculated as follows (Equations (1) to (6)): y = Y/Z (1) y = Y/Z (1) q r = (x2 + y2) (2)

2 2 r = (x + y) 2 2 (2) x = x 1 + K 2 + K 4 + K 6 + K 8 + P r + 2x + 2P xy 1 + P 2 + P 4 (3) 0 1r 2r 3r 4r 2 1 3r 4r 2 2 y0 = y 1 + K r2 + K r4 + K r6 + K r8 + P1 r + 2y + 2P2xy 1 + P r2 + P r4 (4) 1 2 3 4 2 2 3 4 x = x 1 + K 2 + K 4 + K 6 + K 8 + P2r + 2x + 2P1𝑥𝑦 1 + P 2 + P 4 (3) 1r 2r 3r 4r 3r 4r u = w 0.5 + cx + x0f + x0B + y0B (5) × 1 2

v = h 0.5 + c2y + y02f (6) y = y 1 + K 2 + K 4 + K 6 + K 8 × + P1r + 2y + 2P2𝑥𝑦(1 + P 2 + P 4 ) (4) 1r 2r 3r 4r 3r 4r where, X, Y, and Z are the point coordinates in the local camera coordinate system; u and v denote the projected point coordinates in the image coordinate system (in pixels); f is the focal length; cx and u = w × 0.5 + cx + x f + xʹB1 + yʹB2 (5) cy are the principal point offset; K1,K2,K3, and K4 are the radial distortion coefficients; B1 and B2 represent the affinity and non-orthogonality (skew) coefficients, respectively; and w and h are the image width and height in pixels, respectively.v = h × 0.5 + cy + yʹf (6) Table2 shows the residual for the images and interior orientation parameters. The FC350 camera where, X, Y, and Z are the point coordinates in the local camera coordinate system; u and v denote used in Inspire 1 was a wide-angle camera; hence, many residuals went toward the end. FC350 is a the projected point coordinates in the image coordinate system (in pixels); f is the focal length; cx and low-cost camera. As a result, the manufacturing process is not precise, and the residuals can largely be cy are the principal point offset; K1, K2, K3, and K4 are the radial distortion coefficients; B1 and B2 calculated [31]. represent the affinity and non-orthogonality (skew) coefficients, respectively; and w and h are the The processes of extracting feature points using the scale invariant feature transform (SIFT) image width and height in pixels, respectively. technique, building high-density points using the structure from motion (SfM) technique, and Table 2 shows the residual for the images and interior orientation parameters. The FC350 camera converting relative coordinates through the ground control point (GCP) input were required in used in Inspire 1 was a wide-angle camera; hence, many residuals went toward the end. FC350 is a generating an orthophoto. The SIFT method is suitable for feature point extraction because it is low-cost camera. As a result, the manufacturing process is not precise, and the residuals can largely invariant to the changes in image rotation and scale [32,33]. The SfM method connects the feature be calculated [31]. points extracted by two adjacent images, estimates the relative posture and direction of the images matching using epipolar geometry, and estimates the 3D position of the feature points [34,35]. For the GCP input, 19 points were acquired through a virtual reference station survey among global navigation satellite system survey methods. Seven of which were used as GCP, and 12 were used as check points (CPs). The evaluation result using the CPs showed that the orthophoto with a high positional accuracy

Energies 2020, 13, 4213 6 of 12 was produced with horizontal maximum errors of x = 0.11 m and y = 0.15 m and a vertical maximum error of z = 0.16 m (Figure1).

Energies 2020, 13, x FOR PEER REVIEW 6 of 12 Table 2. Residual for images and interior orientation parameters. Table 2. Residual for images and interior orientation parameters. : 2306.73 Focal length (pixel) : 2306.73 Focal length (pixel) : 2308.33 : 2308.33 : 1964.69 Principal point (pixel) : 1964.69 Principal point (pixel) : 1519.39: 1519.39 SkewSkew coefficient coefficient 0.179201 0.179201 Residual for images Residual for images : 0.000909: 0.000909 RadialRadial distortion distortion (mm) (mm) : 0.000324: 0.000324 : −0.137255 : 0.137255 : 0.132394− Tangential distortion (mm) : 0.132394 Tangential distortion (mm) : −0.048854 : 0.048854 : 0.013898− : 0.013898 The processes of extracting feature points using the scale invariant feature transform (SIFT) technique, building high-density points using the structure from motion (SfM) technique, and 2.3. Data Processing converting relative coordinates through the ground control point (GCP) input were required in 2.3.1.generating Red, Green an orthophoto.and Blue (RGB) The SIFT Image method Data Processing is suitable for feature point extraction because it is invariant to the changes in image rotation and scale [32,33]. The SfM method connects the feature pointsFour colorextracted space by techniquestwo adjacent were images, applied estimates herein, the namely relative LRV,posture HSV, and HSL, direction and YUV.of the As images defined by the Internationalmatching using Lighting epipolar Commission, geometry, and the estimates LRV technique the 3D position is an indicator of the feature of light points reflectance [34,35]. For based on CIELABthe GCP ((International input, 19 points Commission were acquired on Illumination through a virtual (CIE)) reference international station color survey standard among (also globa knownl as CIEnavigation L*a*b* orsatellite sometimes system abbreviatedsurvey methods. as simply Seven of “Lab” which color were space)), used as whereGCP, and L* 12 is lightness;were used as a* is red andcheck green; points and b*(CPs). is yellow The evaluation and blue. result Light usingreflectance the CPs is calculatedshowed that using the orthophoto the L of three with values. a highLRV is positional accuracy was produced with horizontal maximum errors of x = 0.11 m and y = 0.15 m and a measure of the amount of visible and available light reflected by all directions and wavelengths when a vertical maximum error of z = 0.16 m (Figure 1). light enters the surface [36,37]. This scale is used to identify the amount of light that color reflects or absorbs.2.3. Data The Processing measurements range from 0% (reflecting visible light) to 100% (absorbing all light). The HSV color space, which is the color space closest to the human intuitive method based on human2.3.1. cognition, Red, Green is and expressed Blue (RGB in hue,) Image saturation, Data Processing and value (brightness). Hue is the relative placement angle ofFour the ringcolor shape space whentechniques the longestwere applied red in herein, the visible namely spectrum LRV, HSV, is HSL, 0◦. Therefore, and YUV. As the defined color values rangeby fromthe International 0◦ to 360◦, with Lighting 360 ◦ Commission,and 0◦ indicating the LRV the technique same color is an red. indicator Saturation of light is the reflectance intensity of a particularbased on color. CIELAB The saturation((International value Commission indicates theon Illumination degree of concentration (CIE)) international when thecolor darkest standard state of a specific(also color known is 100%.as CIE It L*a*b* has the or sometimessame achromatic abbreviated color as when simply the "Lab" saturation color space)) value, is where 0%. Brightness L* is (value)lightness; represents a* is red the and degree green; of and brightness b* is yellow when and whiteblue. Light is 100% reflectance and black is calculated is 0% [38 using]. the L of three values. LRV is a measure of the amount of visible and available light reflected by all directions The HSL color space represents hue, saturation, and lightness (luminance). The color luminance and wavelengths when light enters the surface [36,37]. This scale is used to identify the amount of is a measure of the perceived lightness. Like the HSV color space, the HSL color space is black at one light that color reflects or absorbs. The measurements range from 0% (reflecting visible light) to 100% end;(absorbing however, all it islight). white in the other. The most saturated colors represent a middle value [39]. TheThe YUV HSV color color space space, is generallywhich is the used color in videospace transmission,closest to the human computer intuitive graphics, method and based visualization on in scientifichuman cognition, computing is expressed [40]. It canin hue, also saturation, be considered and value similar (brightness). to the retina Hue is in the the relative human eye. Theplacement luminance angle (Y channel)of the ring describes shape when the the light longest intensity red in just the likevisible the spectrum rod cells is in 0°. the Therefore, retina. Thethe YUV colorcolor space values is suitable range from for 0° black to 360°, and with white 360° display and 0° devices.indicating Two the same channels color calledred. Saturation ”U” and is ”V”the carry colorintensity information of a par fromticular the color. chrominance The saturation components. value indicates The the YUV degree color ofspace concentration has already when separated the luminance;darkest state hence, of a it specific takes upcolor little is 100%. bandwidth It has the compared same achromatic to the RGBcolor colorwhen spacethe saturation [41]. value is 0%. Brightness (value) represents the degree of brightness when white is 100% and black is 0% [38]. Only the elements related to the brightness value were used among the four color space techniques. The HSL color space represents hue, saturation, and lightness (luminance). The color luminance We evaluated only the brightness value because the influence of light absorption and reflection depends is a measure of the perceived lightness. Like the HSV color space, the HSL color space is black at one on theend; degree however, of brightness. it is white in We the calculated other. The itmost using saturated the L channel colors represent in LRV, Va middle channel value in HSV, [39]. L channel in HSL, andThe Y channel YUV color in YUV. space The is four generally color used space in techniques video transmission, were calculated computer using graphics, MATLAB. and Table 3 presentsvisualization the color in expressionscientific computing method [40]. and It formulas. can also be considered similar to the retina in the human eye. The luminance (Y channel) describes the light intensity just like the rod cells in the retina. The YUV color space is suitable for black and white display devices. Two channels called ″U″ and ″V″

EnergiesEnergies 20 202020, ,1 133, ,x x FOR FOR PEER PEER REVIEW REVIEW 77 of of 12 12 EnergiesEnergies 202020 202020, 13,1 13,3 x, xFORFOR FOR PEERPEER PEER REVIEW REVIEW 77 7 ofof of 12 12 carrycarry color color information information from from the the chrominance chrominance compon components.ents. The The YUV YUV color color space space has has already already YUVcarrycarry color color color space information information is suitable from from for theblack the chrominance chrominance and white display compon compon devices.ents.ents. The The Two YUV YUV channels color color called space space ″ hasU has″ and already already ″V″ separatedseparated luminance; luminance; hence, hence, it it takes takes up up little little bandwidth bandwidth compared compared to to the the RGB RGB color color space space [41]. [41]. carryseparatedseparated color luminance; luminance; information hence, hence, from it it takes thetakes chrominance up up little little bandwidth bandwidth components. compared compared The to toYUV the the RGB RGBcolor color color space space space has [41]. [41]. already OnlyOnly the the elements elements related related to to the the brightness brightness value value were were used used among among the the four four color color space space separatedOnlyOnly luminance; the the elements elements hence, related related it takes to to theup the little brightness brightness bandwidth value value compared were were used used to the among among RGB color the the four fourspace color color [41]. space space techniques.techniques. WeWe evaluatedevaluated onlyonly thethe brbrightnessightness valuevalue becausebecause thethe influenceinfluence ofof lightlight absorptionabsorption andand techniques.techniques.Only the We We elements evaluated evaluated related only only the theto br thebrightnessightness brightness value value value because because were the the used influence influence among of of thelight light four absorption absorption color space and and reflectionreflection depends depends on on the the degree degree of of brightness. brightness. We We calculated calculated it it using using the the L L channel channel in in LRV, LRV, V V channel channel techniques.reflectionreflection depends depends We evaluated on on the the degree degreeonly the of of brightness. brightness.brightness We Wevalu calculated calculatede because it it theusing using influence the the L L channel channel of light in in absorptionLRV, LRV, V V channel channel and inin HSV,HSV, LL channelchannel inin HSL,HSL, andand YY channelchannel inin YUV.YUV. TheThe fourfour colorcolor spacespace techniquestechniques werewere calculacalculatedted Energiesreflectioninin HSV, HSV,2020 L Ldepends ,channel 13channel, 4213 onin in theHSL, HSL, degree and and Y ofY channel brightness.channel in in YUV. YUV.We calculated The The four four itcolor color using space space the Ltechniques techniqueschannel in were LRV,were calcula Vcalcula channel7 oftedted 12 usingusing MATLAB. MATLAB. Table Table 3 3 presents presents the the color color expression expression method method and and formulas. formulas. inusingusing HSV, MATLAB. MATLAB. L channel Table Table in HSL, 3 3 presents presents and Y the channelthe color color inexpression expression YUV. The method methodfour color and and space formulas. formulas. techniques were calculated using MATLAB. Table 3 presents the color expression method and formulas. TableTableTable 3.3. 3. Values Values of of of the the the color color color space space space technology technology technology (i.e., (i.e., (i.e., light light light-reﬂectance--reflectancereflectance value value value ( (LRVLRV (LRV),)), ,hue hue saturation saturation hue saturation value value TableTable 3. 3. Values Values of of the the color color space space technology technology (i.e., (i.e., light light-reflectance-reflectance value value ( LRV(LRV),) ,hue hue saturation saturation value value value((HSVHSV) (HSV),), , huehue saturationhue saturation saturation lightness lightness lightness ((HSLHSL (HSL),)), , and and and YUV), YUV), YUV), surface surface surface temperature, temperature, temperature, and and indoor indoor temperature temperature temperature Table(HSV(HSV) ,3.) , hue Valueshue saturation saturation of the color lightness lightness space technology(HSL(HSL),) , and and (i.e., YUV), YUV), ligh surfacet-reflectance surface temperature, temperature, value (LRV), and and hue indoorindoor saturation temperature temperature value accordingaccordingaccording toto to color.color. color. (HSV),accordingaccording hue to to saturationcolor. color. lightness (HSL), and YUV), surface temperature, and indoor temperature according((aa)) to color. (b)(b) (c)(c) (d)(d) (a(a)(a) ) (b)(b) (b) (c) (c) (d)(d)(d)

(a) (b) (c) (d)

Adapted from Li et al. Adapted from Sambyal Adapted from Tsai and AdaptedAdaptedAdapted fromfrom from LiLi Li etet et al.al. al. Adapted from Sambyal Adapted from Tsai and AdaptedAdaptedAdapted fromfrom from IbraheemIbraheemIbraheem Ibraheem AdaptedAdapted(2005) from from [42 Li Li] et et al. al. AdaptedAdapted(2015) from from [ 43Sambyal Sambyal] AdaptedAdaptedTseng (2012)fromfrom Tsai Tsai [44] and and AdaptedAdapted from from Ibraheem Ibraheem (2005)(2005)(2005) [42][42] [42] AdaptedAdapted(2015) from from [43] Sambyal Sambyal AdaptedAdaptedTseng from (2012)from Tsai Tsai [44] and and etet al.al. (2012)(2012) [[45]45] (2005)(2005) [42] [42] (2015) [43] TsengTseng (2012) (2012) [44] [44] etet al. al. (2012) (2012) [45] [45] (2015) [43] TsengTseng (2012) (2012) [44] [44] etet al. al. (2012) (2012) [45] [45] R0 = (2015)R(2015)/255, [43] G [43]0 = G/255, RR′ 0== R/255,R/255, G G′ =0 =G/255,G/255, B′ LRVLRVLRV == = 100100 100 × × ×(L* (L* (L* ++ + RRR′′ ′== = R/255,R/255, R/255, GG G′′ ′== = G/255,G/255, G/255, BB B′′ ′== = RR′ ′= = R/255, R/255, G G′ ′= = G/255, G/255, B B′ ′= = YYYY= == = (0.257 (0.257(0.257 (0.257 ×× × R) R) R) + ++ + (0.504(0.504 (0.504 ×× × LRVLRV = = 100 100 × × 3(L*3 (L* + + RR′ ′=B = 0R/255, R/255,= B/255, G G′ ′= V = G/255, =G/255,Max(R B B′ ′0= ,= RBR′ 0′= = R/255, R/255,B/255, G LG′ ′= = G/255,(R G/255,0 + G B 0B′ +′= = YY = = (0.257 (0.257× × × R) R) + + (0.504 (0.504 × × 16/116)3 3 B/255, V = Max(R′, G′, B′) B/255,= B/255, L = L(R =′ (R+ G′ +′ +G B′ ′+) /3 G) + (0.098 × B) + 16 16/116)1616//116)116)3 3 B/255,B/255, V V = = Max(R Max(R′,′ ,G G′,′ ,B B′)′ ) B/255, L = (R′ + G′ + B′)/3 G)G)G)+ + + (0.098(0.098 (0.098 × × B) B) + + 1616 16 1616/116)/116) B/255,B/255, V V = = GMax(R Max(R0,B0)′,′ ,G G′,′ ,B B′)′ ) B/255,B/255, L L = = B(R (R0′)/3)′/ 3′+ + G G′ ′+ + B B′)′/)3/3 G)G) + + (0.098 (0.098 ×× × B) B) + + 16 16 L*L*: :lightness lightness; ;R: R: red red value; value; G: G: green green value; value; B: B: blue blue value; value; V: V: HSV HSV (V (V channel); channel); L: L: HSL HSL (L (L channel); channel); and and L*:L*L*: :lightness lightness; lightness;R: ;R: R: red red red value; value; value; G: G: G: green green green value; value; value; B: blueB: B: blue blue value; value; value; V: HSV V: V: HSV (VHSV channel); (V (V channel); channel); L: HSL L: L: (L HSL HSL channel); (L (L channel); channel); and Y: YUVand and L*:Y: YUVlightness; (Y channel) R: red value;. G: green value; B: blue value; V: HSV (V channel); L: HSL (L channel); and (YY: channel). YUV (Y channel). Y:Y: YUV YUV (Y (Y channel).channel) channel). . 2.3.2. Thermal Infrared (TIR) Image Data Processing 2.3.2.2.3.2. Thermal Thermal Infrared Infrared (TIR) (TIR) Image Image Data Data Processing Processing 2.3.2.2.3.2. Thermal Thermal Infrared Infrared (TIR)( TIR(TIR) )ImageI mageImage Data Data Processing Processing TheThe colorcolor temperaturetemperature waswas acquiredacquired usingusing InspireInspire 1,1, ZenmuseZenmuse XT640,XT640, andand DJIDJI GOGO application.application. TheThe color color temperature temperature was was acquired acquired using using Inspire Inspire 1, 1, Zenmuse Zenmuse XT640, XT640, and andand DJI DJIDJI GO GO application. application. TheThe DJIThe DJI GO GOcolor application application temperature allows allows was one one acquired to to view view using the the image image Inspire of of 1,a a UAV UAVZenmuse--mountedmounted XT640, TIR TIR and camera camera DJI GO in in real realapplication. time time and and TheThe DJI DJI GO GO application application allows allows one one to to view view the the image image of of a aa UAV UAV-mountedUAV-mounted-mounted TIR TIR camera camera in in real real time time and and Thecheckcheck DJI the the GO temperature. temperature. application The allowsThe data data one were were to view acquired acquired the imag at at a a height eheight of a UAV-mounted of of approximately approximately TIR 50 50 camera m m such such in that that real the the time surface surface and checkcheck the the temperature. temperature. The The data data were were acquired acquired at at a a height height of of approximately approximately 50 50 m m such such that that the the surface surface checktemperaturetemperature the temperature. forfor eacheach colorThecolor data couldcould were bebe acquired seenseen atat once. once.at a height TheThe TIR TIRof approximately imagesimages werewere obtained50obtained m such with withthat thethe surface surfacesurface temperaturetemperature for for eacheach color color could could be be seenseen seen atat at once.once. once. The The TIR TIR images images were were obtainedobtained withwith thethe surface surface temperaturetemperaturetemperature forusing using each Flir Flir color Tools Tools could + + S/W. S/W. be FlirFlirseen Tools Tools at once + + S/W S/W. The conv conv TIRertserts images radiated radiated were values values obtained to to degrees degrees with the Fahrenheit Fahrenheit surface temperaturetemperature using using Flir Flir Tools Tools + + S/W.S S/W./W. Flir Flir Tools Tools ++ + SS/W S/W/W convertsconv convertserts radiated radiated values values to to degrees degrees Fahrenheit Fahrenheit temperatureoror Celsius Celsius and and using provides provides Flir Tools the the maximum, maximum,+ S/W. Flir minimum, minimum,Tools + S/W and and converts average average radiated temperatures temperatures values within withinto degrees a a single single Fahrenheit point point of of oror Celsius Celsius and andand provides providesprovides the thethe maximum, maximum, minimum, minimum, and and and average average average temperatures temperatures temperatures within within within a a single asingle single point point point of of ortemperaturetemperature Celsius and (i.e.,(i.e., provides regionregion the ofof maximu interest).interest).m, TheThe minimum, surfacesurface and temperaturetemperature average temperat ofof fivefive spotsspotsures withinperper colorcolor a single waswas obtainedpointobtained of oftemperaturetemperature temperature (i.e., (i.e., (i.e., region region region of of ofinterest). interest). interest). The The The surface surface surface temperature temperature temperature of of of five five ﬁve spots spots per perper color colorcolor was was obtainedobtained temperaturehereinherein using using (i.e.,Fli Flirr Tools regionTools + + softwareof software interest). (Figure (Figure The surfac5) 5). . e temperature of five spots per color was obtained hereinherein using using FlirFli Flir r Tools Tools ++ + software software (Figure (Figure5 5).5) ).5). .

Figure 5. Temperature acquisition screen of the Flir Tools + software. The closer the color was to red,

the higher the temperature. The closer the color was to blue, the lower the temperature.

3. Results and Discussion Figure6 shows the result obtained when the color space technique was applied using MATLAB. Figure6a represents the LRV, while b represents the HSV. Figure6c depicts the HSL, while d represents Energies 2020, 13, x FOR PEER REVIEW 8 of 12

Figure 5. Temperature acquisition screen of the Flir Tools + software. The closer the color was to red, the higher the temperature. The closer the color was to blue, the lower the temperature.

3. Results and Discussion Energies 2020, 13, 4213 8 of 12 Figure 6 shows the result obtained when the color space technique was applied using MATLAB. Figure 6a represents the LRV, while b represents the HSV. Figure 6c depicts the HSL, while d therepresents YUV. The the highest YUV. valueThe highest was observed value was on theobserv whiteed rooftop,on the whereaswhite rooftop, the lowest whereas appeared the lowest in the black.appeared A comparisonin the black. ofA thecomparison color space of the value colo tor thespace temperature value to the showed temperature that the showed average that value the ofaverage the color value space of the technique color space in white technique (surface in whit temperaturee (surface average: temperature 44.1 average:◦C; indoor 44.1 temperature °C; indoor average:temperature 33.3 average:◦C) with 33.3 the lowest°C) with temperature the lowest was temp 87.7erature (LRV), was 96.1 87.7 (HSV), (LRV), 94.1 96.1 (HSL), (HSV), and 94.1 240 (YUV).(HSL), Theand black240 (YUV). color (averageThe black surface color (average temperature: surface 73.4 temperature:◦C; indoor temperature73.4 °C; indoor average: temperature 37.1 ◦C), average: which recorded37.1 °C), which the highest recorded temperature, the highest showed temperature, average showed values ofaverage 3.7 (LRV), values 25.1 of (HSV), 3.7 (LRV), 22.6 25.1 (HSL), (HSV), and 54.222.6 (YUV).(HSL), and Overall, 54.2 the(YUV). value Overall, increased the asvalue the colorincreased approached as the color white approached and tended white to decrease and tended as the colorto decrease approached as the black.color approached black.

(a) (b)

Figure 6.6. ImageImage processingprocessing result result obtained obtained from from the the color color space space technique technique application: application: (a) LRV; (a) LRV; (b) HSV (b) (VHSV channel); (V channel); (c) HSL (c) (LHSL channel); (L channel); and ( dand) YUV (d) (YYUV channel). (Y channel).

In all the color space techniques, a high value waswas observed on the white roof, while the lowest value was observed in in the the black black roof roof (Table (Table 4).4). Light Light gray gray was was used used to to express express the the color color of of cement cement in inthe the early early days days of ofconstruction. construction. If Ifcement cement is isleft left for for a a long long time time without without proper maintenance after construction, the color may turn dark gray or black. In In this this case, case, the the index index would would be be close close to to black. black. Conversely, ifif the the color color fades fades over over time, time, the the blue blue color color will will show show an index an index close toclose white. to white. Green, Green, which iswhich often is used often as used a general as a waterproofgeneral waterproof paint color, paint does color, not havedoes anot very have low a index very likelow black,index butlike is black, close tobut that is close of black. to that A of comparison black. A comparison of each index of each value index obtained value byobtained the color by spacethe color technique space technique with the surfacewith the and surface indoor and temperature indoor temperature values showed values thatshowed white, that which white, had which the highest had the value highest in thevalue color in spacethe color technique, space technique, had the lowest had the temperature lowest temperat value. Byure contrast, value. By black, contrast, which black, showed which the lowestshowed value the inlowest the color value space in the technique, color space exhibited technique, the exhibite highestd temperaturethe highest temperature value. In the value. color In space the color technique, space thetechnique, color closer the color to white closer showed to white a lowershowed temperature a lower temperature value, while value, the while color the closer color to closer black showedto black ashowed higher a temperature higher temperature value. In value. other In words, other words, the green the and green blue and colors blue colors used as used the as waterproof the waterproof paint causedpaint caused increased increased surface surface and indoor and indoor temperatures temperatures in summer. in summer.

Energies 2020, 13, 4213 9 of 12

Table 4. Values for the color space technology (i.e., LRV, HSV, HSL and YUV), surface temperature, and indoor temperature according to color.

HSV HSL YUV Surface Indoor LRV (V Channel) (L Channel) (Y Channel) Temperature (◦C) Temperature (◦C) White 1 92.2 98.0 96.9 246 43.6 33.0 White 2 87.1 96.1 93.7 240 44.0 33.2 White 3 93.9 98.0 96.1 247 43.6 33.0 White 4 81.4 92.5 89.8 231 45.0 33.5 White 5 83.9 95.7 93.9 236 44.4 34.0 Gray 1 60.7 86.7 82.2 205 56.8 34.0 Gray 2 62.1 87.5 82.9 207 56.0 34.2 Gray 3 63.4 88.2 83.7 209 56.6 34.5 Gray 4 60.0 86.3 81.8 204 56.0 34.0 Gray 5 61.1 86.7 82.0 205 55.8 34.5 Green 1 26.8 62.0 42.5 125 60.5 35.0 Green 2 24.3 59.2 40.8 120 60.6 35.2 Green 3 24.5 59.6 40.2 119 61.0 35.4 Green 4 26.2 61.2 42.4 124 60.4 35.5 Green 5 25.8 60.8 42.0 123 60.6 35.5 Blue 1 5.0 52.2 34.7 62 63.8 35.5 Blue 2 5.2 53.3 35.1 63 64.0 35.4 Blue 3 5.8 53.3 35.9 67 63.5 35.5 Blue 4 5.3 53.3 34.9 63 64.0 36.0 Blue 5 5.3 52.9 35.1 64 63.8 35.2 Black 1 3.7 25.9 23.1 55 73.2 37.0 Black 2 3.6 23.9 22.4 54 73.0 37.5 Black 3 3.6 25.5 22.7 54 74.0 37.0 Black 4 3.2 23.9 21.0 50 73.3 36.8 Black 5 4.2 26.3 23.9 58 73.5 37.2

Pearson correlation and linear regression analyses were used to analyze the correlation between the surface and indoor temperatures for the brightness values of the four color space techniques. The Pearson’s correlation was between 1 and 1. The closer the value to 1, the higher the negative − − correlation. The closer the value to +1, the higher the positive correlation. A value of 0 indicates the absence of a correlation. For the linear regression analysis, the closer the value to 1, the better the suitability of the relationship (Table5).

Table 5. Relationship between the color space technique and the surface temperature.

LRV HSV HSL YUV Pearson correlation analysis 0.93 0.95 0.93 0.92 − − − − Linear regression analysis 0.87 0.91 0.86 0.84

An analysis of the correlation between the values of the four color space techniques and the surface temperature depicted that the Pearson correlations were 0.93 (LRV), 0.95 (HSV), 0.93 (HSL), and − − − 0.93 (YUV), showing that HSV had the highest negative correlation. The linear regression analysis − found 0.87 LRV, 0.91 HSV, 0.86 HSL, and 0.84 YUV, with the HSV expressing the best suitability of the relationship (Table6).

Table 6. Relationship between the color space technique and the indoor temperature.

LRV HSV HSL YUV Pearson correlation analysis 0.91 0.97 0.93 0.91 − − − − Linear regression analysis 0.82 0.94 0.86 0.83

In addition, an analysis of the correlation between the values of the four color space techniques and the indoor temperature showed that the Pearson correlations were 0.91 (LRV), 0.97 (HSV), 0.93 − − − Energies 2020, 13, 4213 10 of 12

(HSL), and 0.91 (YUV), with HSV having the highest negative correlation. Meanwhile, the linear − regression analysis illustrated values of 0.82 (LRV), 0.94 (HSV), 0.86 (HSL), and 0.83 (YUV), showing that HSV had the highest suitability of relationship. In conclusion, the HSV value among the four color space techniques had a higher correlation between the surface and indoor temperatures than the values of the other color space techniques.

4. Conclusions In this study, four color space techniques were applied using the RGB image of the UAV. The brightness values of the color space techniques for each rooftop color were then compared with the surface and indoor temperatures to evaluate the cool roof performance. As a result, in the cool roof, the white color (average surface temperature: 44.1 ◦C; average indoor temperature: 33.3 ◦C) showed the lowest temperature, whereas the black color (average surface temperature: 73.4 ◦C; average indoor temperature: 37.1 ◦C) exhibited the highest temperature. Compared with the color space technique, the white color showed the highest color space technique value, whereas the black color showed the lowest color space technique value. The technique showed a high correlation of the Pearson correlation coefficient with the surface temperature ( 0.95) and linear regression analysis (0.91). It also showed a − high correlation with the indoor temperature ( 0.97) and the linear regression analysis (0.94). These − results verified the evaluation that the RGB images and the HSV techniques can be used to evaluate cool roofs instead of thermometers or infrared sensors. This study deviated from the field approach based on uncertainty and applied the UAV photogrammetry technique, which is a remote-sensing technique. In addition, we confirmed that cool roof performance evaluation is possible even when only a low-cost RGB sensor, instead of an expensive infrared sensor, is used for the UAV. The evaluation of the cool roof technology using the RGB sensor and the HSV color space technique confirmed environmental benefits, such as mitigation of the urban heat island effect, comfort benefits from acceptable indoor thermal conditions, and CO2 reduction through peak power demand reduction. In addition, the evaluation of cool roof performance is essential not only for the content mentioned, but also for solving the deepening tropical phenomenon worldwide. Therefore, an organization like Europe’s Cool Roof Council should also be established in Korea. In a future study, we will proceed with the calculation of the artificial temperature image using a correlation function derived from the HSV value and compare it with TIR images. We will also verify the possibility of evaluating cool roofs with RGB sensors and the color space technology in areas with many buildings.

Author Contributions: Conceptualization, W.H.L.; Software K.L. and J.S.; writing—Original draft preparation, K.L.; writing—review and editing, Y.H. and W.H.L. All authors have read and agreed to the published version of the manuscript. Funding: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2020R1I1A3061750). Also, this research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2019H1A2A1074119). Acknowledgments: The authors thank the anonymous reviewers for their constructive comments and suggestions. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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