Shadow detection of the high- resolution remote sensing image based on pulse coupled neural network

Traditional shadow detection methods are usually detected shadow areas by the single threshold in shadow feature map. This leads to the detection results susceptible to affect by noise, and some special target (high-bright objects and green vegetation etc.) susceptible to misdetection. In this paper, a shadow detection method is proposed based on pulse coupled neural network (PCNN). The model can ignore small differences of pixels values in one area, because the network output is not only associated with the pixel brightness but also associated with pixel spatial location. Firstly, a new shadow feature map is build. Then PCNN model is applied to get optimal detection result with max entropy. The experimental results showed that the proposed model performed better than the single threshold models.