Shadow detection method based on shadow probability model for remote sensing images
LI Pengwei1, GE Wenying2, LIU Guoying2
1. School of software engineering, Anyang Normal University, Anyang Henan 455000, China;
2. School of Computer and Information Engineering, Anyang Normal University, Anyang Henan 455000, China
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 multiresolution Markov Random Field (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 multiscale 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.
李鹏伟, 葛文英, 刘国英. 基于阴影概率模型的遥感影像阴影检测方法[J]. 计算机应用, 2015, 35(2): 510-514.
LI Pengwei, GE Wenying, LIU Guoying. Shadow detection method based on shadow probability model for remote sensing images. Journal of Computer Applications, 2015, 35(2): 510-514.
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2015, Vol. 35 
