Multiresolution-Based Rough Fuzzy Possibilistic <italic>C</italic>-Means Clustering Method for Land Cover Change Detection
Object-oriented change detection (OOCD) plays an important role in remote sensing change detection. Generally, most of current OOCD methods adopt the highest predicted probability to determine whether objects have changes. However, it ignores the fact that only parts of an object have changes, which...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9980384/ |
| _version_ | 1827965561159548928 |
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| author | Tong Xiao Yiliang Wan Jianjun Chen Wenzhong Shi Jianxin Qin Deping Li |
| author_facet | Tong Xiao Yiliang Wan Jianjun Chen Wenzhong Shi Jianxin Qin Deping Li |
| author_sort | Tong Xiao |
| collection | DOAJ |
| description | Object-oriented change detection (OOCD) plays an important role in remote sensing change detection. Generally, most of current OOCD methods adopt the highest predicted probability to determine whether objects have changes. However, it ignores the fact that only parts of an object have changes, which will generate the uncertain classification information. To reduce the classification uncertainty, an improved rough-fuzzy possibilistic <inline-formula><tex-math notation="LaTeX">$c$</tex-math></inline-formula>-means clustering algorithm combined with multiresolution scales information (MRFPCM) is proposed. First, stacked bitemporal images are segmented using the multiresolution segmentation approach from coarse to fine scale. Second, objects at the coarsest scale are classified into changed, unchanged, and uncertain categories by the proposed MRFPCM. Third, all the changed and unchanged objects in previous scales are combined as training samples to classify the uncertain objects into new changed, unchanged, and uncertain objects. Finally, segmented objects are classified layer by layer based on the MRFPCM until there are no uncertain objects. The MRFPCM method is validated on three datasets with different land change complexity and compared with five widely used change detection methods. The experimental results demonstrate the effectiveness and stability of the proposed approach. |
| first_indexed | 2024-04-09T17:36:29Z |
| format | Article |
| id | doaj.art-aa727ad07dfe43298466248467d16dfc |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-04-09T17:36:29Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-aa727ad07dfe43298466248467d16dfc2023-04-17T23:00:13ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352023-01-011657058010.1109/JSTARS.2022.32282619980384Multiresolution-Based Rough Fuzzy Possibilistic <italic>C</italic>-Means Clustering Method for Land Cover Change DetectionTong Xiao0Yiliang Wan1https://orcid.org/0000-0001-7346-3442Jianjun Chen2Wenzhong Shi3https://orcid.org/0000-0002-3886-7027Jianxin Qin4Deping Li5School of Geographical Sciences, Hunan Normal University, Changsha, ChinaSchool of Geographical Sciences, Hunan Normal University, Changsha, ChinaHunan Vocational College of Engineering, Changsha, ChinaDepartment of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, ChinaSchool of Geographical Sciences, Hunan Normal University, Changsha, ChinaSchool of Geographical Sciences, Hunan Normal University, Changsha, ChinaObject-oriented change detection (OOCD) plays an important role in remote sensing change detection. Generally, most of current OOCD methods adopt the highest predicted probability to determine whether objects have changes. However, it ignores the fact that only parts of an object have changes, which will generate the uncertain classification information. To reduce the classification uncertainty, an improved rough-fuzzy possibilistic <inline-formula><tex-math notation="LaTeX">$c$</tex-math></inline-formula>-means clustering algorithm combined with multiresolution scales information (MRFPCM) is proposed. First, stacked bitemporal images are segmented using the multiresolution segmentation approach from coarse to fine scale. Second, objects at the coarsest scale are classified into changed, unchanged, and uncertain categories by the proposed MRFPCM. Third, all the changed and unchanged objects in previous scales are combined as training samples to classify the uncertain objects into new changed, unchanged, and uncertain objects. Finally, segmented objects are classified layer by layer based on the MRFPCM until there are no uncertain objects. The MRFPCM method is validated on three datasets with different land change complexity and compared with five widely used change detection methods. The experimental results demonstrate the effectiveness and stability of the proposed approach.https://ieeexplore.ieee.org/document/9980384/Classification uncertaintyland cover change detection (LCCD)multiresolution segmentationrough fuzzy possibilistic <named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX">$c$</tex-math> </inline-formula> </named-content>-means clustering algorithm (RFPCM) |
| spellingShingle | Tong Xiao Yiliang Wan Jianjun Chen Wenzhong Shi Jianxin Qin Deping Li Multiresolution-Based Rough Fuzzy Possibilistic <italic>C</italic>-Means Clustering Method for Land Cover Change Detection IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Classification uncertainty land cover change detection (LCCD) multiresolution segmentation rough fuzzy possibilistic <named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX">$c$</tex-math> </inline-formula> </named-content>-means clustering algorithm (RFPCM) |
| title | Multiresolution-Based Rough Fuzzy Possibilistic <italic>C</italic>-Means Clustering Method for Land Cover Change Detection |
| title_full | Multiresolution-Based Rough Fuzzy Possibilistic <italic>C</italic>-Means Clustering Method for Land Cover Change Detection |
| title_fullStr | Multiresolution-Based Rough Fuzzy Possibilistic <italic>C</italic>-Means Clustering Method for Land Cover Change Detection |
| title_full_unstemmed | Multiresolution-Based Rough Fuzzy Possibilistic <italic>C</italic>-Means Clustering Method for Land Cover Change Detection |
| title_short | Multiresolution-Based Rough Fuzzy Possibilistic <italic>C</italic>-Means Clustering Method for Land Cover Change Detection |
| title_sort | multiresolution based rough fuzzy possibilistic italic c italic means clustering method for land cover change detection |
| topic | Classification uncertainty land cover change detection (LCCD) multiresolution segmentation rough fuzzy possibilistic <named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX">$c$</tex-math> </inline-formula> </named-content>-means clustering algorithm (RFPCM) |
| url | https://ieeexplore.ieee.org/document/9980384/ |
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