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An adaptive multi-threshold image segmentation algorithm based on object-oriented classification for high-resolution remote sensing images
Author(s): Kai Yu; Jiahang Liu; Zhuanli Lu
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Paper Abstract

The object-oriented segmentation is a critical process in the classification and recognition of high-resolution remote sensing images. Multi-threshold segmentation methods have been widely used in multi-target recognition and information extraction of high-resolution remote sensing images because they are simple, easy-to-implement, and has ideal segmentation effect. However, the determination of thresholds for existing multi-threshold segmentation algorithms is still a problem, which limits to get the best effect of segmentation. To address this issue we propose a self-adapted multi-threshold segmentation method, based on region merging, toward segmenting remote sensing images. This method involves four steps: image preprocessing based on morphological filtering, improved watershed transformation to initiate primitive segments, optimal region merging, and self-adapted multi-threshold segmentation. The performance of the proposed algorithm is evaluated in QuickBird images and compared to the existing region merging method. The results reveal the proposed segmentation method outperforms the existing method, as indicated by its lower discrepancy measure.

Paper Details

Date Published: 24 October 2017
PDF: 8 pages
Proc. SPIE 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications, 104624B (24 October 2017); doi: 10.1117/12.2285511
Show Author Affiliations
Kai Yu, Xi'an Institute of Optics and Precision Mechanics, CAS (China)
Univ. of Chinese Academy of Sciences (China)
Jiahang Liu, Xi'an Institute of Optics and Precision Mechanics, CAS (China)
Zhuanli Lu, Xi'an Institute of Optics and Precision Mechanics, CAS (China)
Univ. of Chinese Academy of Sciences (China)


Published in SPIE Proceedings Vol. 10462:
AOPC 2017: Optical Sensing and Imaging Technology and Applications
Yadong Jiang; Haimei Gong; Weibiao Chen; Jin Li, Editor(s)

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