Heterogeneous Images Change Detection Based on Iterative Joint Global–Local Translation

Most heterogeneous change detection methods based on transfer learning may not yield satisfactory results due to the lack of comprehensive utilization of the global and local characteristics of the image. In this article, we propose an unsupervised heterogeneous change detection method based on iterative joint global–local translation (IJGLT). The two heterogeneous images are first segmented into superpixels with the same boundary. Unlike the transfer learning methods for classification tasks that directly reduce the distance of the distribution of all data, we construct a translation function to decrease the difference between unchanged superpixels while increasing the difference of changed superpixels. During the translation process, the overall distribution is constrained using pseudo change labels, and the local manifold preserving is utilized to maintain the spatial structure and local adjacency relationship of superpixels. By comparing the translated superpixels of the heterogeneous images, the change map is obtained and then refined with pseudo change labels taking an iterative approach until convergence. Using the public and self-made optical-SAR heterogeneous datasets with different resolutions, the experimental results demonstrate that IJGLT is superior to several typical comparison methods with a maximum overall accuracy of 0.98 and a kappa coefficient of 0.9.