Improved Spatiotemporal Information Fusion Approach Based on Bayesian Decision Theory for Land Cover Classification
High-spatial-resolution (HSR) images and high-temporal-resolution (HTR) images have their unique advantages and can be replenished by each other effectively. For land cover classification, a series of spatiotemporal fusion algorithms were developed to acquire a high-resolution land cover map. The fu...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/23/6003 |
| _version_ | 1797462226310791168 |
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| author | Yan Jin Xudong Guan Yong Ge Yan Jia Wenmei Li |
| author_facet | Yan Jin Xudong Guan Yong Ge Yan Jia Wenmei Li |
| author_sort | Yan Jin |
| collection | DOAJ |
| description | High-spatial-resolution (HSR) images and high-temporal-resolution (HTR) images have their unique advantages and can be replenished by each other effectively. For land cover classification, a series of spatiotemporal fusion algorithms were developed to acquire a high-resolution land cover map. The fusion processes focused on the single level, especially the pixel level, could ignore the different phenology changes and land cover changes. Based on Bayesian decision theory, this paper proposes a novel decision-level fusion for multisensor data to classify the land cover. The proposed Bayesian fusion (PBF) combines the classification accuracy of results and the class allocation uncertainty of classifiers in the estimation of conditional probability, which consider the detailed spectral information as well as the various phenology information. To deal with the scale inconsistency problem at the decision level, an object layer and an area factor are employed for unifying the spatial resolution of distinct images, which would be applied for evaluating the classification uncertainty related to the conditional probability inference. The approach was verified on two cases to obtain the HSR land cover maps, in comparison with the implementation of two single-source classification methods and the benchmark fusion methods. Analyses and comparisons of the different classification results showed that PBF outperformed the best performance. The overall accuracy of PBF for two cases rose by an average of 27.8% compared with two single-source classifications, and an average of 13.6% compared with two fusion classifications. This analysis indicated the validity of the proposed method for a large area of complex surfaces, demonstrating the high potential for land cover classification. |
| first_indexed | 2024-03-09T17:33:29Z |
| format | Article |
| id | doaj.art-12679c24c78744f9b28f665726cd2cff |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T17:33:29Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-12679c24c78744f9b28f665726cd2cff2023-11-24T12:04:03ZengMDPI AGRemote Sensing2072-42922022-11-011423600310.3390/rs14236003Improved Spatiotemporal Information Fusion Approach Based on Bayesian Decision Theory for Land Cover ClassificationYan Jin0Xudong Guan1Yong Ge2Yan Jia3Wenmei Li4School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaResearch Center for Digital Mountain and Remote Sensing Application, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, ChinaState Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaSchool of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaSchool of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaHigh-spatial-resolution (HSR) images and high-temporal-resolution (HTR) images have their unique advantages and can be replenished by each other effectively. For land cover classification, a series of spatiotemporal fusion algorithms were developed to acquire a high-resolution land cover map. The fusion processes focused on the single level, especially the pixel level, could ignore the different phenology changes and land cover changes. Based on Bayesian decision theory, this paper proposes a novel decision-level fusion for multisensor data to classify the land cover. The proposed Bayesian fusion (PBF) combines the classification accuracy of results and the class allocation uncertainty of classifiers in the estimation of conditional probability, which consider the detailed spectral information as well as the various phenology information. To deal with the scale inconsistency problem at the decision level, an object layer and an area factor are employed for unifying the spatial resolution of distinct images, which would be applied for evaluating the classification uncertainty related to the conditional probability inference. The approach was verified on two cases to obtain the HSR land cover maps, in comparison with the implementation of two single-source classification methods and the benchmark fusion methods. Analyses and comparisons of the different classification results showed that PBF outperformed the best performance. The overall accuracy of PBF for two cases rose by an average of 27.8% compared with two single-source classifications, and an average of 13.6% compared with two fusion classifications. This analysis indicated the validity of the proposed method for a large area of complex surfaces, demonstrating the high potential for land cover classification.https://www.mdpi.com/2072-4292/14/23/6003land coverBayesian fusionclassificationhigh resolution |
| spellingShingle | Yan Jin Xudong Guan Yong Ge Yan Jia Wenmei Li Improved Spatiotemporal Information Fusion Approach Based on Bayesian Decision Theory for Land Cover Classification Remote Sensing land cover Bayesian fusion classification high resolution |
| title | Improved Spatiotemporal Information Fusion Approach Based on Bayesian Decision Theory for Land Cover Classification |
| title_full | Improved Spatiotemporal Information Fusion Approach Based on Bayesian Decision Theory for Land Cover Classification |
| title_fullStr | Improved Spatiotemporal Information Fusion Approach Based on Bayesian Decision Theory for Land Cover Classification |
| title_full_unstemmed | Improved Spatiotemporal Information Fusion Approach Based on Bayesian Decision Theory for Land Cover Classification |
| title_short | Improved Spatiotemporal Information Fusion Approach Based on Bayesian Decision Theory for Land Cover Classification |
| title_sort | improved spatiotemporal information fusion approach based on bayesian decision theory for land cover classification |
| topic | land cover Bayesian fusion classification high resolution |
| url | https://www.mdpi.com/2072-4292/14/23/6003 |
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