review · Remote Sensing
This review explores the application of deep learning for detecting changes in remote sensing imagery. Remote sensing images offer a valuable method for observing Earth's surface and identifying objects from aerial or satellite perspectives. By combining various heterogeneous data sources, such as multispectral, hyperspectral, radar, and multitemporal imagery, researchers can achieve a more comprehensive understanding of the Earth. This fusion of diverse information significantly improves the accuracy of change detection tasks. The review covers deep learning methods for both homogeneous and heterogeneous scenes, examines publicly available datasets, analyses selected deep learning models, and discusses current challenges, trends, and future developments in the field.
Understanding changes on Earth's surface is crucial for numerous applications, including environmental monitoring, disaster response, and urban planning. This research review helps advance the methods for automatically detecting these changes using satellite and aerial imagery, making the process more efficient and accurate for various stakeholders.
This abstract describes a review of deep learning techniques for change detection in remote sensing. It does not indicate specific application pathways, potential users, or a readiness level for commercialisation, as its focus is on synthesising existing research and identifying future directions.
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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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DOI: 10.3390/rs16203852
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