GeSANet: Geospatial-Awareness Network for VHR Remote Sensing Image Change Detection

The characteristics of very high-resolution (VHR) remote sensing images (RSIs) have higher spatial resolution inherently and are easier to obtain globally compared with hyperspectral images (HSIs), making it possible to detect small-scale land cover changes in multiple applications. RSI change detection (RSI-CD) based on deep learning has been paid attention to become a frontier research field in recent years and is currently facing two challenging problems. The first is high dependence on registration between bitemporal images caused by high spatial resolution. The other is high pseudo-change information response caused by low spectral resolution. In order to address the abovementioned two problems, a novel RSI-CD framework called geospatial-awareness network (GeSANet) based on the geospatial position matching mechanism (PMM) with multilevel adjustment and the geospatial content reasoning mechanism (CRM) with diverse pseudo-change information filtering is proposed. First, the PMM assigns independent 2-D offset coordinates to each position in the previous temporal image. Afterward, bilinear interpolation is employed to obtain the subpixel feature value after the offset, and the sparse results based on the difference are transmitted to the next level prediction to realize multilevel geospatial correction. The CRM extracts the global features from the corrected sparse feature map in terms of dimensions, implementing effective discriminant feature extraction on the basis of the original feature map in a stepwise refinement manner through the cross-dimension exchange mechanism, to filter out various pseudo-change information as well as maintain real change information. Comparison experiments with five recent state-of-the-art (SOTA) methods are carried out on two popular datasets with diverse changes, and the results show that the proposed method has good robustness and validity for multitemporal RSI-CD. In particular, it has a strong comparative advantage in detecting small entity changes and edge details. The source code of the proposed framework can be downloaded from
https://github.com/zxylnnu/GeSANet
.