Optical Remote Sensing Image Understanding With Weak Supervision: Concepts, methods, and perspectives
Optical Remote Sensing Image Understanding With Weak Supervision: Concepts, methods, and perspectives
Yue, J.; Fang, L.; Ghamisi, P.; Xie, W.; Li, J.; Chanussot, J.; Plaza, A.
In recent years, supervised learning has been widely used in various tasks of optical remote sensing image (RSI) understanding, including RSI classification, pixel-wise segmentation, change detection, and object detection. The methods based on supervised learning need a large amount of high-quality training data, and their performance highly depends on the quality of the labels. However, in practical remote sensing applications, it is often expensive and time consuming to obtain large-scale data sets with high-quality labels, which leads to a lack of sufficient supervised information. In some cases, only coarse-grained labels can be obtained, resulting in the lack of exact supervision. In addition, the supervised information obtained manually may be wrong, resulting in a lack of accurate supervision. Therefore, RSI understanding often faces the problems of incomplete, inexact, and inaccurate supervised information, which will affect the breadth and depth of remote sensing applications.
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IEEE Geoscience and Remote Sensing Magazine 10(2022)2, 250-269
DOI: 10.1109/MGRS.2022.3161377
Cited 26 times in Scopus
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