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.

中文翻译：

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弱监督光学遥感图像理解：概念、方法和观点


近年来，监督学习已广泛应用于光学遥感图像（RSI）理解的各种任务中，包括RSI分类、像素级分割、变化检测和目标检测。基于监督学习的方法需要大量高质量的训练数据，其性能很大程度上取决于标签的质量。然而，在实际的遥感应用中，获取具有高质量标签的大规模数据集通常是昂贵且耗时的，这导致缺乏足够的监督信息。在某些情况下，只能获得粗粒度的标签，导致缺乏精确的监督。另外，人工获取的监管信息可能是错误的，导致监管缺乏准确度。因此，RSI理解常常面临监督信息不完整、不精确、不准确的问题，从而影响遥感应用的广度和深度。