Unsupervised Scale-Driven Change Detection With Deep Spatial–Spectral Features for VHR Images

The rapid development of remote sensing technology has enabled the acquisition of very high spatial resolution (VHR) multitemporal images in Earth observation. However, how to effectively exploit these existing data to accurately monitor land surface changes is still a challenging task. In this article, we propose an unsupervised scale-driven change detection (CD) framework for VHR images by jointly analyzing the spatial–spectral change information, which combines the advantages of deep feature learning and multiscale decision fusion. First, a well pretrained deep fully convolutional network (FCN) is used to automatically extract the deep spatial context information from the acquired images. Then, the uncertainty analysis incorporating the deep spatial feature and the image spectral feature is implemented to generate a pseudobinary change map. On this basis, it is easy to choose suitable samples to train an excellent support vector machine (SVM) classifier, thus detecting changes occurred on the ground. In addition, the multiscale superpixel segmentation technique is introduced to make full use of the spatial structural information, which takes an image-object as the basic analysis unit. Finally, a robust binary change map with high detection precision can be achieved by merging the CD results obtained at different scales. The impressive experimental results on four real data sets demonstrate the effectiveness and flexibility of the proposed framework.