Abstract
Semantic segmentation has several applications in remote sensing, including but not limited to land use classification, change detection, and environmental monitoring. The main challenge in accepting the segmentation algorithms is that there are no labeled data over different terrains and geographies. Pixel-level labeling calls for particular knowledge and experience due to the possibility that each pixel in a satellite image could represent a sizable portion of the globe. Using CNN, U-Net, Res2-Unet, and other machine learning models, we examined earlier research in this study, which included semantic segmentation of satellite pictures. In this study, the literature is assessed using experimental dataset and evaluation metrics like as precision, recall, f1-score, IoU, and mIoU. Also, common issues with semantic segmentation of satellite images were investigated. We also looked into potential future research in this field.
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Mehta, Y., Katkar, V., Patel, S. (2023). Semantic Segmentation of Optical Satellite Images. In: Murthy, B.K., Reddy, B.V.R., Hasteer, N., Van Belle, JP. (eds) Decision Intelligence. InCITe 2023. Lecture Notes in Electrical Engineering, vol 1079. Springer, Singapore. https://doi.org/10.1007/978-981-99-5997-6_9
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