Raft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features

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• 作者：Min Wang^a ; ^b ; ^{sysj0918@126.com} ; Qi Cui^a ; ^b ; ^{547646661@qq.com} ; Jie Wang^a ; ^b ; ^{wangjieyl@sina.com} ; Dongping Ming^c ; ^{Mingdp@cugb.edu.cn} ; Guonian Lv^a ; ^b ; ^{gnlu@njnu.edu.cn}
• 关键词：Region-line primitive association framework ; Raft cultivation ; High resolution remote sensing ; Information extraction ; Multi-scale ; Object-based image analysis
• 刊名：ISPRS Journal of Photogrammetry and Remote Sensing
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：123
• 期：Complete
• 页码：104-113
• 全文大小：5350 K
• 卷排序：123

文摘

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.