归一化阴影植被指数NSVI的构建及其应用效果
投稿时间:2017-09-16  修订日期:2017-10-26  点此下载全文
引用本文:许章华,林璐,王前锋,黄旭影,刘健,余坤勇,陈崇成.归一化阴影植被指数NSVI的构建及其应用效果[J].红外与毫米波学报,2018,37(2):154~162].XU Zhang-Hua,LIN Lu,WANG Qian-Feng,HUANG Xu-Ying,LIU Jian,YU Kun-Yong,CHEN Chong-Li.Construction and application effects of normalized shaded vegetation index (NSVI)[J].J.Infrared Millim.Waves,2018,37(2):154~162.]
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作者单位E-mail
许章华 福州大学 环境与资源学院 fafuxzh@163.com 
林璐 福州大学 环境与资源学院  
王前锋   
黄旭影   
刘健   
余坤勇   
陈崇成   
基金项目:国家自然科学基金项目(面上项目,重点项目,重大项目)
中文摘要:以ALOS AVNIR-2、CBERS-02B CCD、HJ1A-CCD2、Landsat 7 ETM四幅中分辨率遥感影像为试验数据,分析明亮区植被、阴影区植被与水体区的光谱特征与差异,基于近红外波段与归一化植被指数NDVI,构建归一化阴影植被指数NSVI,并评价其光谱差异增强及分类效果.结果表明,NSVI大幅扩大了明亮区植被、阴影区植被、水体区间的光谱相对差异,降低光谱混淆概率;利用NSVI阈值法对四幅试验影像进行分类,总精度均大于97%,总Kappa在0.96以上,且阴影区植被的检测精度均在94%以上,总Kappa系数亦高于0.96.该指数利用地物在近红外波段的辐射差异,解决NDVI只能部分削弱地形影响的问题,扩大地物间的光谱差异,从而提升地物尤其是阴影检测的有效性,且不存在NDVI“易饱和”问题,可为遥感影像阴影去除提供一种新的解决方案.
中文关键词:归一化阴影植被指数  明亮区植被  阴影区植被  水体区  阴影检测
 
Construction and application effects of normalized shaded vegetation index (NSVI)
Abstract:The spectral features and differences in bright vegetation area, shaded vegetation area and water area were investigated by the experimental data from four medium resolution remote sensing images of ALOS AVNIR-2, CBERS-02B CCD, HJ1A-CCD2 and Landsat 7 ETM. Based on the near-infrared band and normalized difference vegetation Index (NDVI), Normalized Shaded Vegetation Index (NSVI) was constructed and the enhancements of spectral differences and classification effect were also evaluated. The results show that NSVI has increased the relative diferences of the spectra in bright vegetation area, shaded vegetation area and water area, and reduced probability of misapplication for the spectral data. The NSVI threshold method was employed to classify the four experimental images. The overall accuracy is over 97%, and the overall Kappa coefficient is above 0.96. The detection accuracy of the shaded vegetation area is over 94% and the Kappa coefficient is also higher than 0.96. By using radiation differences of the near-infrared band between the ground objects, NSVI can solve the problem that NDVI can only partially weaken the topographic effect and enlarge the spectral differences among the ground objects. NSVI enhances the validity of the ground objects especially in the shadow detection and avoids the “saturation” problem of NDVI. It can provide a new solution to remove the shadow in remote sensing images.
keywords:normalized shaded vegetation index, bright vegetation area, shaded vegetation area, water area, shadow detection
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