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REMOTE SENSING AND DEEP LEARNING INTEGRATION FOR SPATIAL INTELLIGENCE
Abstract
This review article provides an overview of the combination of remote sensing with deep learning techniques in the last ten years. It specifically examines the emerging patterns and applications in both fields, highlighting their combined use in processing remote sensing data. It focuses on how these techniques have brought about significant changes in environmental monitoring, urban planning, agricultural management, security, and change detection. The article discusses various satellite probes, detailing their specific capabilities, technological attributes, and suitability for diverse observational tasks. Also, it stops attention on multispectral fusion techniques aimed to integrate data from multiple spectral bands or sensors to enhance the overall quality of remote sensing imagery. Additionally, it provides an overview of potential neural network architectures, highlighting the necessity for innovative algorithms that can effectively manage the growing amount and diversity of remote sensing datasets. The discussion revolves around the authors- aspirations for future research, employing advanced deep learning models for understanding complex spatial and spectral patterns.
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References16
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