Information Extraction of High-Resolution Remotely Sensed Image Based on Multiresolution Segmentation

Peng Shao (), Guodong Yang, Xuefeng Niu, Xuqing Zhang, Fulei Zhan and Tianqi Tang
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Guodong Yang: College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China
Xuefeng Niu: College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China
Xuqing Zhang: College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China
Fulei Zhan: College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China
Tianqi Tang: College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China

Sustainability, 2014, vol. 6, issue 8, 1-11

Abstract: The principle of multiresolution segmentation was represented in detail in this study, and the canny algorithm was applied for edge-detection of a remotely sensed image based on this principle. The target image was divided into regions based on object-oriented multiresolution segmentation and edge-detection. Furthermore, object hierarchy was created, and a series of features (water bodies, vegetation, roads, residential areas, bare land and other information) were extracted by the spectral and geometrical features. The results indicate that the edge-detection has a positive effect on multiresolution segmentation, and overall accuracy of information extraction reaches to 94.6% by the confusion matrix.

Keywords: edge-detection; object-oriented; multiresolution segmentation; spectral features; geometrical features; confusion matrix (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2014
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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