遥感
情态动词
变更检测
计算机科学
融合
人工智能
图像融合
计算机视觉
地质学
图像(数学)
材料科学
语言学
哲学
高分子化学
作者
Souad Saidi,Soufiane Idbraim,Younes Karmoude,Antoine Masse,Manuel Arbeló
出处
期刊:Remote Sensing
[Multidisciplinary Digital Publishing Institute]
日期:2024-10-17
卷期号:16 (20): 3852-3852
被引量:13
摘要
Remote sensing images provide a valuable way to observe the Earth’s surface and identify objects from a satellite or airborne perspective. Researchers can gain a more comprehensive understanding of the Earth’s surface by using a variety of heterogeneous data sources, including multispectral, hyperspectral, radar, and multitemporal imagery. This abundance of different information over a specified area offers an opportunity to significantly improve change detection tasks by merging or fusing these sources. This review explores the application of deep learning for change detection in remote sensing imagery, encompassing both homogeneous and heterogeneous scenes. It delves into publicly available datasets specifically designed for this task, analyzes selected deep learning models employed for change detection, and explores current challenges and trends in the field, concluding with a look towards potential future developments.
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