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Change Detection of SAR images based on Convolution Neural Network with Curvelet Transform

Change detection is an essential task to study the changes of earth surfaces in remote sensing. It is extensively being investigated in SAR images nowadays. However, SAR images suffer from the speckle noise, which is a major drawback. To address the speckle noise problem, we propose the convolutional neural network with curvelet transform. As curvelet transform can suppress the noise, it is applied in preclassification, where the difference image is transformed using curvelet. Further, transformed image is classified by hierarchical fuzzy c-means (FCM) clustering, where each pixel is classified into different classes like changed class and unchanged class. From the preclassification, patches centered at the pixels belonging to these classes are generated as the training samples. Moreover, these training samples are passed through median filter before sending them to the convolutional neural network (CNN). The median filter helps in the reduction of noise. After the training of the CNN model, the trained model classifies the image pixels and provides the final binary change map. The experimental results obtained from the two SAR datasets confirm the effectiveness of the proposed method.