Multiscale Superpixel Segmentation With Deep Features for Change Detection

In this paper, a novel change detection technique is proposed based on multiscale superpixel segmentation and stacked denoising autoencoders (SDAE). This approach is designed to achieve superpixel-based change detection, in which the basic analysis unit is between pixel-based and object-based ones. Given two original images, the difference image (DI) is obtained by conventional DI generation methods. Then, we propose a multiscale superpixel segmentation which is guided by the changing degrees estimated from the DI. Different from traditional multiscale superpixel, the proposed multiscale superpixel segmentation is employed in a single map. In the proposed method, SDAE is used to learn the difference representation between bi-temporal superpixels. Bi-temporal superpixels are stacked and fed into SDAE for its pre-training, and then SDAE is fine-tuned according to pseudo labels generated by traditional unsupervised methods. After fine-tuned with back propagation, the SDAE can be used to classify all superpixel pairs into changed or unchanged ones. The experimental results on real remote sensing datasets have demonstrated the effectiveness of the proposed approach.