Improved Spatiotemporal Information Fusion Approach Based on Bayesian Decision Theory for Land Cover Classification
文献类型：期刊论文
| 作者 | Jin, Yan1,4; Guan, Xudong3; Ge, Yong2; Jia, Yan1,4; Li, Wenmei1,4 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2022-12-01 |
| 卷号 | 14期号:23页码:29 |
| 关键词 | land cover Bayesian fusion classification high resolution |
| DOI | 10.3390/rs14236003 |
| 通讯作者 | Guan, Xudong(guanxd@imde.ac.cn) |
| 英文摘要 | 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. |
| WOS关键词 | TIME-SERIES ; IMAGE FUSION ; REFLECTANCE FUSION ; NEURAL-NETWORKS ; MULTISOURCE ; CLASSIFIERS ; ACCURACY ; MODELS |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000896169800001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/187911]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Guan, Xudong |
| 作者单位 | 1.Nanjing Univ Posts & Telecommun, Sch Geog & Biol Informat, Nanjing 210023, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Chinese Acad Sci, Inst Mt Hazards & Environm, Res Ctr Digital Mt & Remote Sensing Applicat, Chengdu 610041, Peoples R China 4.Smart Hlth Big Data Anal & Locat Serv Engn Lab Jia, Nanjing 210023, Peoples R China |
| 推荐引用方式 GB/T 7714 | Jin, Yan,Guan, Xudong,Ge, Yong,et al. Improved Spatiotemporal Information Fusion Approach Based on Bayesian Decision Theory for Land Cover Classification[J]. REMOTE SENSING,2022,14(23):29. |
| APA | Jin, Yan,Guan, Xudong,Ge, Yong,Jia, Yan,&Li, Wenmei.(2022).Improved Spatiotemporal Information Fusion Approach Based on Bayesian Decision Theory for Land Cover Classification.REMOTE SENSING,14(23),29. |
| MLA | Jin, Yan,et al."Improved Spatiotemporal Information Fusion Approach Based on Bayesian Decision Theory for Land Cover Classification".REMOTE SENSING 14.23(2022):29. |
入库方式： OAI收割
来源：地理科学与资源研究所
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