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摘要： 针对高分辨率遥感影像中阴影检测精度易受水体、植被等因素干扰的问题，通过分析高分二号影像中典型地物的光谱特征，构建了一种集成特征分量与面向对象分类相结合的阴影检测方法。构建的特征分量包括：主成分第一分量PC1、亮度分量I、归一化差分植被指数NDVI及水体指数WI。将各特征分量进行归一化处理，建立包含波段均值、标准差等特征的规则集，对影像的I和PC1分量进行多尺度分割 ，结合面向对象的方法进行阴影检测。选取不同区域遥感影像进行实验，实验结果表明:与传统基于像素的阴影提取方法相比，该方法提取出的阴影斑块完整，且能有效地减弱水体和植被的影响。 关键词： 阴影检测;  特征分量;  面向对象分类;  GF-2影像     Abstract: The shadow detection accuracy in the high-resolution remote sensing images is easily disturbed by water, vegetation and so on. This study proposed a shadow detection method based on object-oriented method and established characteristic components by analyzing the spectral characteristics of typical features in GF-2 satellite images．The following components were constructed to detect shadow information: first principal component (PC1), brightness component I, Normalized Difference Vegetation Index (NDVI) and Water Index (WI). And then, we normalized each characteristic component to establish a rule set containing features such as band mean, standard deviation. Brightness I and PC1 were chosen as the main data source for multi-resolution segmentation, at last, performed object-oriented method on the segmented images to detect shadow. Selected different areas of GF-2 images for the proposed method, and experimental results show that the proposed method could extract complete shadow patches and effectively reduce the influence of water bodies and vegetation compared with pixel-based method.

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