Abstract
Deep learning approaches are gaining popularity in image feature analysis and in attaining state-of-the-art performances in scene classification of remote sensing imagery. This article presents a comprehensive review of the developments of various computer vision methods in remote sensing. There is currently an increase of remote sensing datasets with diverse scene semantics; this renders computer vision methods challenging to characterize the scene images for accurate scene classification effectively. This paper presents technology breakthroughs in deep learning and discusses their artificial intelligence open-source software implementation framework capabilities. Further, this paper discusses the open gaps/opportunities that need to be addressed by remote sensing communities.
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CITATION STYLE
Tombe, R., & Viriri, S. (2023, March 1). Remote Sensing Image Scene Classification: Advances and Open Challenges. Geomatics. Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/geomatics3010007
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