EATDer: Edge-Assisted Adaptive Transformer Detector for Remote Sensing Change Detection

• Published in: IEEE transactions on geoscience and remote sensing 2024, Vol.62, p.1-15
• Main Authors: Ma, Jingjing, Duan, Junyi, Tang, Xu, Zhang, Xiangrong, Jiao, Licheng
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Branches; Change detection; Change detection (CD); Coders; Convolutional neural networks; Decoding; Detection; Edge detection; Feature extraction; Image edge detection; Image processing; Information processing; Neural networks; Receptive field; Remote sensing; remote sensing (RS); Task analysis; transformer; Transformers; Visualization
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2023.3344083

Change detection (CD) is one of the important research topics in remote sensing (RS) image processing. Recently, convolutional neural networks (CNNs) have dominated the RSCD community. Many successful CNN-based models have been proposed, and they achieved cracking performance. Nevertheless, influenced by the limited receptive field, the CNN-based models are not good at capturing long-distance context dependencies within RS images, negatively impacting their performance. With the appearance of the visual transformer, the above problems have been mitigated. However, the high time costs of the transformer-based models limit their applicability. In addition, previous CD networks (whether CNN-based or transform-based) do not pay attention to the edges of changed areas, reducing the quality of change maps. To overcome the shortcomings discussed above, we propose a new CD method named edge-assisted adaptive transformer detector (EATDer). EATDer consists of a Siamese encoder and an edge-aware decoder. Each branch in the Siamese encoder encloses three self-adaption vision transformer (SAVT) blocks, which aim to capture the local and global information within RS images. Also, two branches are connected by full-range fusion modules (FRFMs), which focus on mining the temporal clues among bi-temporal RS images and pointing out the changed/unchanged messages. The edge-aware decoder first integrates the multiscale features obtained by the encoder using a restoring block. Then, it enhances the combined features by a refining block. Finally, based on the refined features, both the change and edge detection results can be produced. Along with a joint loss function, we can get high-quality change maps in which the changed areas are correct and have clear and smooth edges. The usefulness of our EATDer is validated by extensive experiments conducted on three popular RSCD datasets. Our source codes are available at https://github.com/TangXu-Group/Remote-Sensing-Image-Change-Detection/tree/main/EATDer