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

Abstract

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 and become a frontier research field in recent years, and is currently facing two challenging problems: The first is high dependence on registration between bi-temporal images caused by high spatial resolution; The other is high pseudo-change information response caused by low spectral resolution. In order to address the above-mentioned two problems, a novel RSI-CD framework called Geospatial-Awareness Network (GeSANet) based on the geospatial Position Matching Mechanism (PMM) with multi-level adjustment and the geo-spatial Content Reasoning Mechanism (CRM) with diverse pseudo-change information filtering is proposed. First of all, the PMM assigns independent two-dimensional offset coordinates to each position in the previous temporal image, afterwards, 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 multi-level geospatial correction. The CRM extracts global features from the corrected sparse feature map in terms of dimensions, implementing effective discriminant feature extraction on 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 SOTA methods are carried out on two popular datasets with diverse changes, the results show that the proposed method has good robustness and validity for multi-temporal 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.