  In recent years, supervised learning has been widely used in various tasks of
optical remote sensing image understanding, including remote sensing image
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, remote
sensing image understanding often faces the problems of incomplete, inexact,
and inaccurate supervised information, which will affect the breadth and depth
of remote sensing applications. In order to solve the above-mentioned problems,
researchers have explored various tasks in remote sensing image understanding
under weak supervision. This paper summarizes the research progress of weakly
supervised learning in the field of remote sensing, including three typical
weakly supervised paradigms: 1) Incomplete supervision, where only a subset of
training data is labeled; 2) Inexact supervision, where only coarse-grained
labels of training data are given; 3) Inaccurate supervision, where the labels
given are not always true on the ground.
