Unsupervised Change Detection Using Fuzzy Topology-Based Majority Voting
Remote sensing change detection (CD) plays an important role in Earth observation. In this paper, we propose a novel fusion approach for unsupervised CD of multispectral remote sensing images, by introducing majority voting (MV) into fuzzy topological space (FTMV). The proposed FTMV approach consist...
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doaj-b2d573f10ec745cbabc1d74e995b804d2021-08-26T14:17:31ZengMDPI AGRemote Sensing2072-42922021-08-01133171317110.3390/rs13163171Unsupervised Change Detection Using Fuzzy Topology-Based Majority VotingPan Shao0Wenzhong Shi1Zhewei Liu2Ting Dong3College of Computer and Information Technology, China Three Gorges University, Yichang 443002, ChinaDepartment of Land Surveying and Geo-Informatics, Smart Cities Research Institute, The Hong Kong Polytechnic University, Hong Kong 999077, ChinaDepartment of Land Surveying and Geo-Informatics, Smart Cities Research Institute, The Hong Kong Polytechnic University, Hong Kong 999077, ChinaCollege of Computer and Information Technology, China Three Gorges University, Yichang 443002, ChinaRemote sensing change detection (CD) plays an important role in Earth observation. In this paper, we propose a novel fusion approach for unsupervised CD of multispectral remote sensing images, by introducing majority voting (MV) into fuzzy topological space (FTMV). The proposed FTMV approach consists of three principal stages: (1) the CD results of different difference images produced by the fuzzy C-means algorithm are combined using a modified MV, and an initial fusion CD map is obtained; (2) by using fuzzy topology theory, the initial fusion CD map is automatically partitioned into two parts: a weakly conflicting part and strongly conflicting part; (3) the weakly conflicting pixels that possess little or no conflict are assigned to the current class, while the pixel patterns with strong conflicts often misclassified are relabeled using the supported connectivity of fuzzy topology. FTMV can integrate the merits of different CD results and largely solve the conflicting problem during fusion. Experimental results on three real remote sensing images confirm the effectiveness and efficiency of the proposed method.https://www.mdpi.com/2072-4292/13/16/3171remote sensingunsupervised change detectionfuzzy topologymajority votingconflict management |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Pan Shao Wenzhong Shi Zhewei Liu Ting Dong |
spellingShingle |
Pan Shao Wenzhong Shi Zhewei Liu Ting Dong Unsupervised Change Detection Using Fuzzy Topology-Based Majority Voting Remote Sensing remote sensing unsupervised change detection fuzzy topology majority voting conflict management |
author_facet |
Pan Shao Wenzhong Shi Zhewei Liu Ting Dong |
author_sort |
Pan Shao |
title |
Unsupervised Change Detection Using Fuzzy Topology-Based Majority Voting |
title_short |
Unsupervised Change Detection Using Fuzzy Topology-Based Majority Voting |
title_full |
Unsupervised Change Detection Using Fuzzy Topology-Based Majority Voting |
title_fullStr |
Unsupervised Change Detection Using Fuzzy Topology-Based Majority Voting |
title_full_unstemmed |
Unsupervised Change Detection Using Fuzzy Topology-Based Majority Voting |
title_sort |
unsupervised change detection using fuzzy topology-based majority voting |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2021-08-01 |
description |
Remote sensing change detection (CD) plays an important role in Earth observation. In this paper, we propose a novel fusion approach for unsupervised CD of multispectral remote sensing images, by introducing majority voting (MV) into fuzzy topological space (FTMV). The proposed FTMV approach consists of three principal stages: (1) the CD results of different difference images produced by the fuzzy C-means algorithm are combined using a modified MV, and an initial fusion CD map is obtained; (2) by using fuzzy topology theory, the initial fusion CD map is automatically partitioned into two parts: a weakly conflicting part and strongly conflicting part; (3) the weakly conflicting pixels that possess little or no conflict are assigned to the current class, while the pixel patterns with strong conflicts often misclassified are relabeled using the supported connectivity of fuzzy topology. FTMV can integrate the merits of different CD results and largely solve the conflicting problem during fusion. Experimental results on three real remote sensing images confirm the effectiveness and efficiency of the proposed method. |
topic |
remote sensing unsupervised change detection fuzzy topology majority voting conflict management |
url |
https://www.mdpi.com/2072-4292/13/16/3171 |
work_keys_str_mv |
AT panshao unsupervisedchangedetectionusingfuzzytopologybasedmajorityvoting AT wenzhongshi unsupervisedchangedetectionusingfuzzytopologybasedmajorityvoting AT zheweiliu unsupervisedchangedetectionusingfuzzytopologybasedmajorityvoting AT tingdong unsupervisedchangedetectionusingfuzzytopologybasedmajorityvoting |
_version_ |
1721190304211533824 |