中文摘要：

阴影是山地丘陵区遥感影像最为普遍的干扰因素，去除阴影有助于提高影像解译和地物识别的准确性和有效性。构建了阴影植被指数（SVI），并提出应用波段回归模型法实现HJ-1多光谱影像阴影的去除。将该方法应用于试验区HJ-1数据，结果表明：SVI可增大山地丘陵区水体、阴影区及明亮区之间的差异，利用阈值法可以实现影像阴影的有效检测；相关分析显示，各波段拟合模型R^2均在0．80以上；比较阴影去除前、后影像的统计指标说明，在植被最为敏感，即受阴影影响最为严重的近红外波段，随着阴影的去除，波段平均值有了较大幅度的增大；去阴影后影像的标准差均比原影像要小，尤其是在近红外波段。试验结果表明，SVI对山地丘陵区HJ-1影像阴影的检测效果较好，而波段回归模型法可以较为有效地实现阴影的去除。

英文摘要：

The shadow is the most common interference factor of remote sensing image in mountainous and hilly area, so shadow removal is helpful to the improvement of accuracy and effectiveness for image interpretation and feature recognition. Shaded vegetation index （SVI） was constructed, and the band regression model was built for the shadow removal. The proposed method was applied in HJ - 1 multi-spectral image. The results showed that SVI could increase the differences among water, shaded area and bright area. The threshold method could be used to effectively detect the shadow in the image. The correlation analysis showed that R2 of each band regression models was above 0.80. The comparison of image statistical indicators before and after the shadow removal indicated that, the band mean value increased significantly with the removal of shadow at the near-infrared band influenced by shadow and vegetations. The standard deviations of shadow-removal image were lower than those of the original image, especially at the near-infrared band. The testing results showed SVI had good detection effects for the shadow of H J-1 image in mountainous and hilly area, and the band regression model method could effectively remove the shadow.