标题:Kernel Fuzzy C Means Clustering with New Spatial Constraints
作者:Wang, Limei ;Niu, Sijie ;Geng, Leilei
通讯作者:Wang, Limei
作者机构:[Wang, Limei ;Niu, Sijie ] School of Information Science and Engineering, University of Jinan, Jinan, China;[Geng, Leilei ] School of Computer Science 更多
会议名称:6th International Conference on Artificial Intelligence and Security,ICAIS 2020
会议日期:17 July 2020 through 20 July 2020
来源:Communications in Computer and Information Science
出版年:2020
卷:1253 CCIS
页码:3-14
DOI:10.1007/978-981-15-8086-4_1
关键词:Change detection; Difference image; KFCM_S; SAR
摘要:Kernel fuzzy c-means clustering with spatial constraints (KFCM_S) is one of the most convenient and effective algorithms for change detection in synthetic aperture radar (SAR) images. However, this algorithm exists problems of weak noise-immunity and detail-preserving on account of the failure to use spatial neighborhood information. In order to overcome above problems, this paper proposed an algorithm using bilateral filtering and large scale median filtering instead of the original spatial constraints. In particular, the approach uses different calculation methods of constraint terms at different locations of image. The bilateral filtering value is used as spatial neighborhood information at the boundary region for preserving the boundary information while the large scale median filtering value is used at the non boundary region for facilitating noise removal. In this paper, 3 remote sensing datasets are used to verify the proposed approach, and the results show that the proposed approach improves the accuracy of remote sensing image change detection. © 2020, Springer Nature Singapore Pte Ltd.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091536743&doi=10.1007%2f978-981-15-8086-4_1&partnerID=40&md5=38e5c8316df0d4804e239612c17b3081
TOP