基于阴影概率模型的遥感影像阴影检测方法

Shadow detection method based on shadow probability model for remote sensing images

阴影区像素光谱响应的不一致性使得依据阈值获取的阴影检测结果与真实情况出入较大。针对这一问题,结合不透明度和亮度两种信息设计了一个全新的阴影概率模型。在此基础上,针对邻域像素信息未能充分利用的问题,提出了一个基于多尺度马尔可夫随机场(MRF)的遥感影像检测方法。首先,用所提出的模型描述多尺度影像中像素的阴影概率;然后,使用Potts模型建模多尺度标记场,同时兼顾尺度内和尺度间的邻域像素的交互关系;最后,基于最大后验(MAP)概率准则获取最终阴影检测结果。通过与色调亮度比值方法、差分双阈值方法、主成分分析法和支持向量机分类方法的对比实验证明,所提出的方法能够提高高分辨率城区遥感影像的阴影检测精度。

The inhomogeneous spectral response of shadow area makes the shadow detection methods based on threshold always produce results with much difference with real situations. In order to overcome this problem, a new shadow probability model was proposed by combining opacity and intensity. To eliminate the neglection of interaction between neighboring pixels, a method based on muhiresolution Markov Random Fieht （MRF） was proposed for shadow detection of remote sensing images. First, the proposed probability model was used to describe the shadow probability of pixels in the multiresolution images. Then, the Potts model was employed to model muhiscale label fields. Finally, the detection result was obtained by Maximizing A Posteriori （MAP） probability. This method was compared with some shadow detection methods, e.g., the hue/intensity- based method, the difference dual-threshold method and Support Vector Machine （SVM） classifier. The experimental results reveal that the proposed method can improve the accuracy of shadow detection for high-resolution urban remote sensing images.