中文摘要：

阴影是遥感影像中普遍存在的干扰因素，如何有效去除阴影已成为共识，寻找一个有效的阴影检测指标是实现影像阴影去除的基础工作。以LandsatTM，ALOSAVNIR-2，CBERS-02BCCD及HJ-1CCD影像为试验数据，立足于进一步增大阴影区植被与明亮区植被、水体间的差异，实现影像阴影的有效检测，构建了一个新的植被指数——阴影植被指数SVI，该指数既可保证明亮区植被、阴影区植被、水体区在近红外波段的绝对差异，又能对NDVI进行放大，消除可能存在的混淆现象，改变NDVI直方图的“偏态”现象，使植被指数值更接近于正态分布，更符合地面实际；该指数适用于近红外波段辐射特征差异较大的地物。采用精度评估法验证SVI对明亮区植被、阴影区植被、水体区三类地物的识别效果，结果显示，四幅影像总分类精度依次高达98．89％，100％，97．78％，97．78％，总Kappa系数依次为0．9833，1，0．9667，0．9667，说明SVI对明亮区植被、阴影区植被及水体区具有极好的检测效果；对子影像、SVI与NDVI的统计指标对比亦说明，SVI可靠、有效，可以将其应用于影像阴影去除。

英文摘要：

Taking the images of Landsat TM, ALOS AVNIR-2, CBERS-02B CCD and HJ-1 CCD as the experimental data, for increasing the differences among shaded area, bright area and water further, the present paper construed a novel vegetation in- dex-Shaded Vegetation Index（SVI）, which can not only keep the absolute differences among bright area, shaded area and water area in the near-infrared band, but also can enlarge NDVI, eliminate the possible mixes, and change the histogram ＂skewed＂ phenomenon of NDVI, so the vegetation index value is closer t.o normal distribution, and more in line with the filed condition; this new index was applied to the surface features of large difference of the near-infrared radiation characteristics. Verified by ac- curacy assessment for the bright area, shaded area and water area recognition effects with SVI, it was showed that the overall classification accuracies of these images were up to 98. 89M, 100%, 97.78% and 97.78% respectively, with the overall Kappa statistics of 0. 983 3, 1, 0. 966 7, and 0. 966 7, indicating that SVI has excellent detection effects for bright area, shaded area and water area; the statistical comparison of sub-images between SVI and NDVI also illustrated the reliability and effectiveness of SVI, which can be applied in the shadow removal for remote sensing images.