SAR Image Change Detection Based on Deep Denoising and CNN

In this study, an end-to-end deep de-noising model is first designed to remove the noise of SAR images. The intrinsic noise of synthetic aperture radar (SAR) images has a big influence to the image processing performance, especially in change detection (CD). 

With the help of abundant simulated SAR images, deep de-noising model is trained effectively achieved by removing this noise component from the original SAR image. At last, DI is classified into changed and unchanged areas by a three-layer Convolutional Neural Network (CNN). Three real SAR image pairs demonstrate the effectiveness of the proposed method.