Journal
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
Volume 123, Issue -, Pages 104-113Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.isprsjprs.2016.10.008
Keywords
Region-line primitive association; framework; Raft cultivation; High resolution remote sensing; Information extraction; Multi-scale; Object-based image analysis
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Funding
- National Natural Science Foundation of China [41671341, 41371347]
- Natural Science Foundation of Jiangsu Province, China [BK20140042]
- Six Talent Peaks Project in Jiangsu Province [2015-XXRJ-010]
- Jiangsu Surveying, Mapping and Geoinformation Project [JSCHKY201503]
- Qing Lan Project
- Priority Academic Program Development of Jiangsu Higher Education Institutions
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In this paper, we first propose several novel concepts for object-based image analysis, which include line based shape regularity, line density, and scale-based best feature value (SBV), based on the region-line primitive association framework (RLPAF). We then propose a raft cultivation area (RCA) extraction method for high spatial resolution (HSR) remote sensing imagery based on multi-scale feature fusion and spatial rule induction. The proposed method includes the following steps: (1) Multi-scale region primitives (segments) are obtained by image segmentation method HBC-SEG, and line primitives (straight lines) are obtained by phase-based line detection method. (2) Association relationships between regions and lines are built based on RLPAF, and then multi-scale RLPAF features are extracted and SBVs are selected. (3) Several spatial rules are designed to extract RCAs within sea waters after land and water separation. Experiments show that the proposed method can successfully extract different-shaped RCAs from HR images with good performance. (C) 2016 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
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