Extended Random Walker for Shadow Detection in Very High Resolution Remote Sensing Images

The existence of shadows in very high resolution satellite images obstructs image interpretation and the following applications, such as target detection and recognition. Traditional shadow detection methods consider only the pixel-level properties, such as color and intensity of image pixels, and thus, may produce errors around object boundaries. To overcome this problem, a novel shadow detection algorithm based on extended random walker (ERW) is proposed by jointly integrating both shadow property and spatial correlations among adjacent pixels. First, a set of training samples is automatically generated via an improved Otsu-based thresholding method. Then, the support vector machine is applied to obtain an initial detection map, which categorizes all the pixels in the scene into shadow and nonshadow. Finally, the initial detection map is refined with the ERW model, which can simultaneously characterize the shadow property and spatial information in satellite images to further improve shadow detection accuracy. Experiments performed on five real remote sensing images demonstrate the superiority of the proposed method over several state-of-the-art methods in terms of detection accuracy.