High resolution representation‐based Siamese network for remote sensing image change detection

Abstract

Change detection of high-resolution remote sensing images can help to accurately understand the changes in the earth's surface. Advanced methods based on deep features have some limitations, including limited accuracy, poor detection effect, and poor robustness. The main reason is that these frameworks have poor feature extraction capabilities, insufficient context aggregation, and inadequate discrimination capabilities. In order to solve these problems, SiHDNet, a Siamese segmentation network based on deep, high-resolution differential feature interaction, is proposed. Specifically, after the high-resolution features of the dual-temporal image are extracted, the difference map is generated through a special fusion module, which contains sufficient and effective change information. Finally, the final binary change map is obtained through the improved spatial pyramid pooling module. Experiments are conducted on the newly released building change detection data set LEVIR-CD and the challenging remote sensing image change detection data set Google Data Set. Five benchmark methods are chosen. The results of quantitative analysis and qualitative comparison show that SiHDNet is superior to the five benchmark methods. The results of the ablation experiment also verify the effectiveness of this method.