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Raft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features
- Source :
- ISPRS Journal of Photogrammetry and Remote Sensing. 123:104-113
- Publication Year :
- 2017
- Publisher :
- Elsevier BV, 2017.
-
Abstract
- 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.
- Subjects :
- 010504 meteorology & atmospheric sciences
Computer science
0211 other engineering and technologies
02 engineering and technology
computer.software_genre
01 natural sciences
Image (mathematics)
Computer vision
Computers in Earth Sciences
Engineering (miscellaneous)
021101 geological & geomatics engineering
0105 earth and related environmental sciences
Remote sensing
Rule induction
business.industry
Image segmentation
Object (computer science)
Atomic and Molecular Physics, and Optics
Computer Science Applications
Information extraction
Feature (computer vision)
Line (geometry)
Artificial intelligence
Scale (map)
business
computer
Subjects
Details
- ISSN :
- 09242716
- Volume :
- 123
- Database :
- OpenAIRE
- Journal :
- ISPRS Journal of Photogrammetry and Remote Sensing
- Accession number :
- edsair.doi...........af20bd7eef0b170752acab94b9ef2e55