The management and application of remotely sensed data has become much more difficult due to the dramatically growing volume of remotely sensed imagery. To address this issue, content-based image retrieval (CBIR) has been applied to remote sensing image retrieval for information mining. As a consequence of the growing volume of remotely sensed imagery, the number of different types of image-derived products (such as land use/land cover (LULC) databases) is also increasing rapidly. Nevertheless, only a few studies have addressed the exploration and information mining of these products. In this letter, for the sake of making the most use of the LULC map, we propose an approach for the retrieval of alike scenes from it. Based on the proposed approach, we design a content-based map retrieval (CBMR) system for LULC. The main contributions of our work are listed below. Firstly, the proposed system can allow the user to select a region of interest as the reference scene with variable shape and size. In contrast, in the traditional CBIR/CBMR systems, the region of interest is usually of a fixed size, which is equal to the size of the analysis window for extracting features. In addition, the user can acquire various retrieval results by specifying the corresponding parameters. Finally, by combining the signatures in the base signature library, the user can acquire the retrieval result faster.
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