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Remote Sensing for Land & Resources    2021, Vol. 33 Issue (2) : 55-65     DOI: 10.6046/gtzyyg.2020233
Construction and application effect of normalized shadow vegetation index NSVI based on PROBA/CHRIS image
HU Xinyu1,2(), XU Zhanghua1,2,3,4,5,6(), CHEN Wenhui1,2, CHEN Qiuxia7, WANG Lin1,2,3,4, LIU Hui1,2,3,4, LIU Zhicai1,2,3,4
1. School of Environment and Resources, Fuzhou University, Fuzhou 350108, China
2. Research Center of Geography and Ecological Enviroment, Fuzhou University Fuzhou 350108, China
3. Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou 350108, China
4. Key Laboratory of Remote Sensing Monitoring and Assessment and Disaster Prevention of Soil and Water Loss, Fujian Province, Fuzhou 350108, China
5. University Key Lab for Geomatics Technology & Optimize Resource Utilization in Fujian Province, Fuzhou 350002, China;
6. Postdoctoral Research Station of Information and Communication Engineering, Fuzhou University, Fuzhou 350108, China
7. School of Public Administration, Fujian Agriculture and Forestry University, Fuzhou 350002, China
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Shadow is a common interference factor in remote sensing image interpretation in mountainous and hilly areas. The study of shadow detection in hyperspectral remote sensing images is helpful to removing shadow and giving full play to its advantage of hyperspectral resolution. Taking the multi-angle hyperspectral image PROBA/CHRIS as the data source, this paper tries to increase the spectral differences among three typical ground objects, namely, bright area vegetation, shadow area vegetation and water area, selects the characteristic bands by using the sequential projection algorithm (SPA), and analyzes the spectral characteristics of typical ground objects in the original band of CHRIS image and normalized difference vegetation index. Therefore, the normalized shaded vegetation index (NSVI) of the image is constructed. The reasonable threshold is set based on the step-size method, and the images are classified. The ability of NSVI to detect CHRIS shadow is evaluated from two aspects of classification accuracy and spectral difference enhancement effect. The results show that B9 and B15 can be used as the characteristic bands for constructing NSVI of CHRIS images by using SPA to select the band subset with the smallest root-mean-square error (RMSE) and discard the edge bands. CHRIS multi-angle images are classified based on NSVI threshold method. The classification accuracy of three kinds of land in each angle image is above 94%, and the total Kappa is higher than 0.89. The classification effect of 0° image is the best. The sub-images of the three classified land objects are obtained through the mask, and the spectral mean values of the sub-images are different. However, considering the standard deviation, it is found that the spectral overlap phenomenon is obvious, which indicates that NSVI can enhance the spectral differences among typical land objects and improve the separability between spectral confusion pixels. By further comparing the shadow detection effects of NSVI with NDUI and SI, it also proves the shadow detection ability of NSVI, which shows that the constructed NSVI can be applied to shadow detection of PROBA/CHRIS hyperspectral image and can provide important support for shadow removal and shadow information restoration of this image.

Keywords PROBA/CHRIS image      normalized shaded vegetation index (NSVI)      shadow detection      hyperspectral remote sensing      spectral characteristics     
ZTFLH:  TP79  
Corresponding Authors: XU Zhanghua     E-mail:;
Issue Date: 21 July 2021
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Xinyu HU
Zhanghua XU
Wenhui CHEN
Qiuxia CHEN
Zhicai LIU
Cite this article:   
Xinyu HU,Zhanghua XU,Wenhui CHEN, et al. Construction and application effect of normalized shadow vegetation index NSVI based on PROBA/CHRIS image[J]. Remote Sensing for Land & Resources, 2021, 33(2): 55-65.
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Fig.1  Preprocessed various angle images of false color synthesis with B14(R),B6(G),B2(B)
名称 代表波段 中心波长/nm
B1 490.8
绿 B2 552.1
B3,B4,B5,B6,B7,B8,B9,B10,B11,B12,B13 632.5,670.4,681.5,690.0,695.9,701.8,707.9,714.1,720.3,736.6,746.8
近红外 B14,B15,B16,
Tab.1  Band classification by wavelength
Fig.2  Comparison of bands of bright area vegetation, shadow area vegetation, water body under different angle images
Fig.3  NDVI from different angle images
Fig.4  NDVI and histogram of each angle image
Fig.5  NSVI and histogram of each angle image
0 7.255 -0.250 -2.264 0.573
36 3.252 -0.198 -1.697 0.489
-36 3.093 -0.216 -1.560 0.664
55 7.329 -0.198 -1.968 0.223
-55 4.396 -0.020 -1.640 0.539
Tab.2  Comparison of kurtosis and skewness of NDVI and NSVI gray histograms of various angle images
Fig.6  Threshold results selected by step size method
Fig.7  Five different angle image classification results
类型 36° -36° 55° -55°
明亮区植被 NSVI≥0.28 NSVI≥0.26 NSVI≥0.26 NSVI≥0.32 NSVI≥0.32
阴影区植被 0.06<NSVI<0.28 0.1<NSVI<0.26 0.1<NSVI<0.26 0.12<NSVI<0.32 0.12<NSVI<0.32
水体区 NSVII≤0.06 NSVI≤0.1 NSVI≤0.1 NSVI≤0.12 NSVI≤0.12
Tab.3  Best threshold determining of bright vegetation area, shaded vegetation area and water area
类别 生产者
总精度/% Kappa
0 明亮区植被 92.91 99.16 96.33 0.938 2
阴影区植被 99.28 94.52
水体区 97.06 94.29
36 明亮区植被 91.41 98.32 95.67 0.926 9
阴影区植被 98.56 93.84
水体区 100.00 94.29
-36 明亮区植被 91.41 98.32 95.67 0.927 6
阴影区植被 98.56 93.84
水体区 100.00 94.29
55 明亮区植被 93.55 97.48 95.67 0.927 6
阴影区植被 97.14 93.79
水体区 91.67 91.67
-55 明亮区植被 93.44 95.00 94.02 0.899 5
阴影区植被 95.10 93.79
水体区 91.67 91.67
Tab.4  Accuracy assessment of different ground objects extracted from five angles
Fig.8-1  Comparison of mean and standard deviation of three kinds of ground objects in five angle images
Fig.8-2  Comparison of mean and standard deviation of three kinds of ground objects in five angle images
Fig.9  Monitoring Results of Shaded Areas of NDUI and SI
Fig.10  Comparison of different shadow detection methods
区域 原始影像 NSVI指数 NDUI指数 SI指数
Tab.5  Shadow detection results in different areas are enlarged
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