Spatial-Temporal Evolution Guided Change Detection Network for Remote Sensing Images

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 17; pp. 14080 - 14092
• Main Authors: Wang, Qingwang, Hong, Zheng, Huang, Jiangbo, Zhao, Xiaobin, Song, Jian, Zeng, Kai, Shi, Jianwu, Shen, Tao
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Change detection; Change detection (CD); convolutional neural network; Decoding; deep learning (DL); Evolution; Feature extraction; Image color analysis; Imaging; Information processing; Interference; Remote sensing; Seasonal distribution; Spatial distribution; spatial-temporal evolution; Spatiotemporal data; Standardization; Task analysis; very high resolution (VHR) image
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2024.3439510

With the rapid advancement of remote sensing technology, bitemporal remote sensing change detection (CD) techniques have also seen significant progress. However, existing CD tasks still face two challenges: 1) Variations in lighting and seasonal factors complicate imaging conditions, causing pseudovariation interference, and 2) the spatial distribution and shapes of building are diverse, leading to difficulties in extracting and utilizing effective change features. In this article, we propose the spatial-temporal evolution guided change detection network (STEGNet) to capture and fully utilize rich spatial-temporal information. Specifically, we develop the chrono colorizer to mitigate pseudovariant interference by standardizing color styles and enriching time series information. In addition, we introduce the temporal-spatial guidance module, which combines spatial-temporal information to guide the decoding operation and mitigate information loss during spatial-temporal fusion, resulting in finer prediction results. Experimental results on three benchmark datasets demonstrate that STEGNet effectively suppresses pseudovariation interference and significantly improves the integrity of detection boundaries.