Object-level change detection of multi-sensor optical remote sensing images combined with UNet++ and multi-level difference module

With the rapid development of sensor technology, change detection based on multi-sensor optical remote sensing images has become a research hotspot in the field of remote sensing. Due to the differences of sensor imaging, different patterns of manifestation are shown in multi-sensor optical remote sensing images for one scene, leading to a more obvious problem of "pseudo change". Therefore, an object-level change detection method for multi-sensor optical remote sensing images combining UNet++ and multi-stage difference module is proposed in this paper. Firstly, multi-scale feature extraction difference (MFED) module is proposed by this method to enhance the ability of the model to identify "pseudo change". On this basis, multi-scale feature outputs by UNet++ network are used for multi-angle meticulous depiction. Adaptive evidence credibility indicator (AECI) is proposed as well. At last, image segmentation and Dempster-Shafer (DS) theory are combined to design weighted Dempster-Shafer evidence fusion (WDSEF), so as to achieve mapping from pixel-level output of deep network to object-level results. Experiment was conducted to four sets of high-resolution multi-sensor optical image datasets from different regions, and contrastive analysis was conducted to multiple methods of advanced deep learning. The results revealed that, the overall accuracy (OA) and F, score of the proposed method reached more than 91.92% and 63.31%, respectively, under different conditions of spatial resolution and temporal phase difference, which were significantly better than the comparison methods in both visual analysis and quantitative evaluation. © 2023 SinoMaps Press. All rights reserved.