By taking advantages of fine details obtained by the improved spatial resolution, very high-resolution images are promising for detecting change regions and identifying change patterns. However, high overlaps between different change patterns and the complexities of multiclass classification make it difficult to reliably separate change features. A framework named simultaneous change region and pattern identification (SCRAPI) is proposed for simultaneously detecting change regions and identifying change patterns, whose components are aimed at capturing overlaps between change patterns and reducing overlaps driven by user-specific interests. To validate the effectiveness of SCRAPI, a supervised approach is illustrated within this framework, which starts with modeling the relationship between change features by interclass couples and intraclass couples, followed by metric learning where structural sparsity is captured by the mixed norm. Experiments demonstrate the effectiveness of the proposed approach.
Change detection of VHR (Very High Resolution) images is very difficult due to the impacts caused by the seasonal
changes, the imaging condition, and so on. To address the above difficulty, a novel unsupervised change detection
algorithm is proposed based on deep learning, where the complex correspondence between the images is established by
Auto-encoder Model. By taking advantages of the powerful ability of deep learning in compensating the impacts
implicitly, the multi-temporal images can be compared fairly. Experiments demonstrate the effectiveness of the proposed
Remote sensing image registration is the key issue for change detection. To reduce the effects of misregistration on the
accuracy of change detection, a hybrid registration method is proposed in this paper. First, we use the registration
approach based on SIFT(Scale Invariant Feature Transform) to get the initial parameters, and then area-based method is
employed to refine the performance of registration. In order to improve the efficiency of computation, the
multiresolution based coarse-to-fine strategy is adopted during the refined procedure. In contrast with feature-based or
area-based method, our hybrid method is accurate, robust and automated since it integrates the merits of both
approaches. The experiments on simulated and real images show the promising performance of the proposed method.