结合局部分类水平集与颜色特征的遥感影像阴影检测

为提高对非匀质阴影与亮阴影的检测效果，提出结合局部分类水平集与颜色特征的遥感影像阴影检测方法，首先，结合阴影区域的亮度非均匀性，采用局部分类水平集分割遥感图像的阴影区域；然后，通过分析绿地与阴影颜色特征分量的差别以去除候选阴影区中被误检的绿地.实验结果表明所提出的方法优于现有的黑体辐射模型与自适应特征选择法，有效克服了传统方法对非匀质阴影与亮阴影的漏检问题，且整个检测过程无需人工干预.

Shadow Detection of Remote Sensing Images Based on Local-classification Level Set and Color Feature

To detect the intensity inhomogeneous shadows and bright shadows in remote sensing images, this paper proposes a shadow detection method combining level set with the feature of color space. Based on the brightness nonuniformity of shadow regions in remote sensing images, the local classification level set segmentation model is firstly adopted to obtain the preliminary shadow regions. Then the color feature difference between shadow regions and green ones is analyzed and used to distinguish green space from the preliminary shadow areas. Experimental results show that the proposed method is superior to the existing methods such as blackbody radiation model and adaptive feature selection. The omitted error of the proposed method is low since the inhomogeneous shadows and bright ones are effectively detected, and the whole detecting process is done without manual intervention.