An Adaptive Change Threshold Selection Method Based on Land Cover Posterior Probability and Spatial Neighborhood Information
Change threshold selection (CTS) plays an important role in land cover change detection. The traditional CTS methods are mainly based on the information contained in grayscale histogram distributions or pixel neighborhoods. However, land cover is highly spatially heterogeneous, and changes in differ...
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2021
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oai:doaj.org-article:cfd69ca4061d40dd81549050a3c8e9b02021-11-25T00:00:09ZAn Adaptive Change Threshold Selection Method Based on Land Cover Posterior Probability and Spatial Neighborhood Information2151-153510.1109/JSTARS.2021.3124491https://doaj.org/article/cfd69ca4061d40dd81549050a3c8e9b02021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9599380/https://doaj.org/toc/2151-1535Change threshold selection (CTS) plays an important role in land cover change detection. The traditional CTS methods are mainly based on the information contained in grayscale histogram distributions or pixel neighborhoods. However, land cover is highly spatially heterogeneous, and changes in different land cover types are characterized by different magnitudes. Unfortunately, few CTS studies have considered the effects of both land cover type and spatial heterogeneity on CTS, potentially leading to false alarms or missed alarms. To address this challenge, we propose an adaptive CTS method based on land cover posterior probability and spatial neighborhood information (LCSN). First, the posterior probability of the change magnitude in each land cover type is calculated according to a Bayesian criterion to integrate the land cover type information. Second, the posterior probability is calculated using a bilateral filtering method to construct the spatial surface based on the land cover type and spatial neighborhood information. Finally, the degree of difference between the spatial surface and the change magnitude map is taken as the final threshold. The proposed LCSN method is verified with Landsat 8-Operational Land Imager images and IKONOS images. The experimental results show that the LCSN method is effective in reducing the pseudo changes and identifying changes in land cover types with low grayscale values in the corresponding change magnitude maps.Huaqiao XingLinye ZhuYongyu FengWei WangDongyang HouFei MengYuanlong NiIEEEarticleBilateral filteringchange detectionclass probabilitythreshold selectionOcean engineeringTC1501-1800Geophysics. Cosmic physicsQC801-809ENIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 11608-11621 (2021) |
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DOAJ |
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Bilateral filtering change detection class probability threshold selection Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 |
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Bilateral filtering change detection class probability threshold selection Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 Huaqiao Xing Linye Zhu Yongyu Feng Wei Wang Dongyang Hou Fei Meng Yuanlong Ni An Adaptive Change Threshold Selection Method Based on Land Cover Posterior Probability and Spatial Neighborhood Information |
| description |
Change threshold selection (CTS) plays an important role in land cover change detection. The traditional CTS methods are mainly based on the information contained in grayscale histogram distributions or pixel neighborhoods. However, land cover is highly spatially heterogeneous, and changes in different land cover types are characterized by different magnitudes. Unfortunately, few CTS studies have considered the effects of both land cover type and spatial heterogeneity on CTS, potentially leading to false alarms or missed alarms. To address this challenge, we propose an adaptive CTS method based on land cover posterior probability and spatial neighborhood information (LCSN). First, the posterior probability of the change magnitude in each land cover type is calculated according to a Bayesian criterion to integrate the land cover type information. Second, the posterior probability is calculated using a bilateral filtering method to construct the spatial surface based on the land cover type and spatial neighborhood information. Finally, the degree of difference between the spatial surface and the change magnitude map is taken as the final threshold. The proposed LCSN method is verified with Landsat 8-Operational Land Imager images and IKONOS images. The experimental results show that the LCSN method is effective in reducing the pseudo changes and identifying changes in land cover types with low grayscale values in the corresponding change magnitude maps. |
| format |
article |
| author |
Huaqiao Xing Linye Zhu Yongyu Feng Wei Wang Dongyang Hou Fei Meng Yuanlong Ni |
| author_facet |
Huaqiao Xing Linye Zhu Yongyu Feng Wei Wang Dongyang Hou Fei Meng Yuanlong Ni |
| author_sort |
Huaqiao Xing |
| title |
An Adaptive Change Threshold Selection Method Based on Land Cover Posterior Probability and Spatial Neighborhood Information |
| title_short |
An Adaptive Change Threshold Selection Method Based on Land Cover Posterior Probability and Spatial Neighborhood Information |
| title_full |
An Adaptive Change Threshold Selection Method Based on Land Cover Posterior Probability and Spatial Neighborhood Information |
| title_fullStr |
An Adaptive Change Threshold Selection Method Based on Land Cover Posterior Probability and Spatial Neighborhood Information |
| title_full_unstemmed |
An Adaptive Change Threshold Selection Method Based on Land Cover Posterior Probability and Spatial Neighborhood Information |
| title_sort |
adaptive change threshold selection method based on land cover posterior probability and spatial neighborhood information |
| publisher |
IEEE |
| publishDate |
2021 |
| url |
https://doaj.org/article/cfd69ca4061d40dd81549050a3c8e9b0 |
| work_keys_str_mv |
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1718414722374565888 |