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Automatic change detection of specific images acquired in different periods is the main problem of remote sensing image research. Adaptive median filter (AMF) is used to remove the noise in remote sensing image, and Tamura and law mask methods are used to extract the secondary features of the image. The study area is divided into vegetation, water area and urban area. The enhanced back propagation neural network (ebpnn) is used to classify the feature extraction results and realize the change detection of remote sensing images in different periods. Compared with the existing FFNN and CNN classification techniques, ebpnn can effectively detect the changes in the image and has better detection performance.