SAR image change detection system and method based on sparse auto-encoder and convolution neural network

The invention proposes a SAR image change detection system and a method based on a sparse auto-encoder and a convolution neural network, which belong to the field of SAR image processing. The method comprises: extracting characteristics from difference graphs through the sparse auto-encoder (SAE) and then using the FCM clustering to perform difference graph clustering according to the image characteristics to obtain an initial classification result; in combination with the difference graphs and the initial classification result, training the convolution neural network; and through the well-trained CNN, fine-tuning the initial classification result to obtain a final classification result graph. This method makes full use of the characteristics information and the neighborhood information of image pixels to further improve the accuracy of the change detection results. The simulation result shows that compared with the traditional algorithms such as KI and FCM, the SAR image change detection method based on the