变更检测
计算机科学
注意力网络
比例(比率)
遥感
人工智能
深度学习
频道(广播)
高分辨率
图像分辨率
地图学
地理
电信
作者
Yuyang Mr. Cai,Shuhong Liao,Wenxuan He,Weiliang Huang,Jingwen Yan,Lei Liu
标识
DOI:10.1080/01431161.2023.2257860
摘要
ABSTRACTDeep learning has revolutionized change detection (CD) in remote sensing tasks. However, high-resolution CD based on deep learning faces challenges in handling semantic complexity in bi-temporal images, especially across varying weather and lighting conditions. While CNN-based approaches optimize network structures to enhance contextual information, recent attention-based methods increase computational demands. This study introduces the Channel-Spatial Attention Network (CSANet) to extract multi-scale and semantic information from images. Evaluated on LEVIR-CD and DSIFN-CD datasets, CSANet outperforms several state-of-the-art methods, demonstrating its potential for advanced change detection in high-resolution remote sensing.KEYWORDS: Change detectionremote sensingattentionCSANet Disclosure statementNo potential conflict of interest was reported by the author(s).
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