Fuzzy clustering algorithms for unsupervised change detection in remote sensing images

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Self-Supervised Learning in Remote Sensing: A review (2022, methodology): “In remote sensing, unsupervised learning has also been actively employed in various applications, e.g., in scene clas-siﬁcation [40, 38, 41], semantic segmentation [42, 43, 44], change detection [45, 46] and multi-sensor data analysis [47, 48].”
Black hole: A new heuristic optimization approach for data clustering (2013, methodology): “Clustering techniques have been used in many areas such as image processing [17,47,65,85], document clustering [6,8,59], geophysics [29,81], prediction [7,11], marketing and costumer analysis [55,83], agriculture [68], security and crime detection [32], medicine [35,42,56], anomaly detection [25,69]”
Change detection from remotely sensed images: From pixel-based to object-based approaches (2013, background): “Also it does not consider the spatial correlation between the neighboring pixels (Ghosh et al., 2011).”