Heterogeneous Image Change Detection Based on Two-Stage Joint Feature Learning

Heterogeneous image change detection, in contrast to homogeneous image change detection, has been a research hotspot due to the information complementary of different imaging mechanisms. However, the imaging difference leads to challenges on change detection by image comparison. To address the incomparability among heterogeneous images and improve the efficiency of heterogeneous image change detection, this paper proposes a novel heterogeneous image change detection method based two-stage joint feature learning. Assuming that the change is few and the image differences in unchanged areas between heterogeneous images are related to the imaging and environmental differences, it maps heterogeneous images into a similar feature space for comparison. Firstly, the bi-temporal similar feature maps with high similarity are extracted after joint feature learning of heterogeneous image. And the similar feature maps are used for joint feature learning optimized by a similarity measure in order to map them to an approximate feature space for comparison. Then the change map is obtained by segmenting the difference between the optimal feature maps. The experiments prove its superiority over existing methods on two heterogeneous image datasets (optical and synthetic aperture radar (SAR) images).