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
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.
胡新宇, 许章华, 陈文慧, 陈秋霞, 王琳, 刘辉, 刘智才. 基于PROBA/CHRIS影像的归一化阴影植被指数NSVI构建与应用效果[J]. 国土资源遥感, 2021, 33(2): 55-65.
HU Xinyu, XU Zhanghua, CHEN Wenhui, CHEN Qiuxia, WANG Lin, LIU Hui, LIU Zhicai. Construction and application effect of normalized shadow vegetation index NSVI based on PROBA/CHRIS image. Remote Sensing for Land & Resources, 2021, 33(2): 55-65.
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2021, Vol. 33 
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