Abstract
High-resolution remote sensing (HRS) image analysis is a fundamental but challenging problem. To bridge the semantic gap, scene understanding has been proposed to achieve higher-level interpretation, through classifying the HRS scene through spatial relationship cognition and semantic induction between the land-cover objects. As a new research field, however, there has not yet been a study expatiating and summarizing the current situation of scene understanding. This paper first defines the concept of scene understanding for HRS imagery, which is different from natural image scene classification. The theory of scene understanding for HRS imagery is investigated, and is classified into four main categories: 1) scene classification based on semantic objects; 2) scene classification based on mid-level features; 3) scene classification based on deep learning; and 4) scene understanding applications based on geographic data mining.
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