A shadow detection of remote sensing images based on statistical texture features
基于统计混合模型的遥感影像阴影检测

Shadow detection for high spatial resolution remote sensing images is very critical for image segmentation, feature extraction, image matching, automatic target detection and target location. In order to improve the accuracy of shadow detection, we propose a new shadow detection method based on a statistical mixture model, which combines several radial basis function neural networks. Four statistical features, including energy, entropy, contrast and inverse difference moment, extracted from grey level concurrence matrix are used as the model input features. EM-like algorithm is adopted to estimate the model parameters through optimizing the system cost function. Comparative experiments are performed between the Gaussian background model and the histogram threshold method. Experimental results show that higher detection accuracy of the proposed approach is obtained. The proposed method can solve the problem such as high refl ective regions and false alarms in the presence of water, as well as the repeated threshold calculation.