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An adaptive multi-Threshold image segmentation algorithm based on object-oriented classification for high-resolution remote sensing images
Kai, Yu1,2; Jiahang, Liu1; Lu, Zhuanli1,2
2017
会议名称Applied Optics and Photonics China: Optical Sensing and Imaging Technology and Applications, AOPC 2017
会议录名称AOPC 2017: Optical Sensing and Imaging Technology and Applications
卷号10462
会议日期2017-06-04
会议地点Beijing, China
出版者SPIE
产权排序1
摘要

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 multithreshold 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. © 2017 SPIE.

作者部门遥感与智能信息系统研究中心
DOI10.1117/12.2285511
收录类别EI ; ISTP
ISBN号9781510614055
语种英语
ISSN号0277786X
引用统计