{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T11:53:59Z","timestamp":1772798039429,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2017,11,21]],"date-time":"2017-11-21T00:00:00Z","timestamp":1511222400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61331016"],"award-info":[{"award-number":["61331016"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41371342"],"award-info":[{"award-number":["41371342"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the National Key Research and Development Program of China","award":["2016YFC0803003-01"],"award-info":[{"award-number":["2016YFC0803003-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Contradictions in spatial resolution and temporal coverage emerge from earth observation remote sensing images due to limitations in technology and cost. Therefore, how to combine remote sensing images with low spatial yet high temporal resolution as well as those with high spatial yet low temporal resolution to construct images with both high spatial resolution and high temporal coverage has become an important problem called spatio-temporal fusion problem in both research and practice. A Multi-Dictionary Bayesian Spatio-Temporal Reflectance Fusion Model (MDBFM) has been proposed in this paper. First, multiple dictionaries from regions of different classes are trained. Second, a Bayesian framework is constructed to solve the dictionary selection problem. A pixel-dictionary likehood function and a dictionary-dictionary prior function are constructed under the Bayesian framework. Third, remote sensing images before and after the middle moment are combined to predict images at the middle moment. Diverse shapes and textures information is learned from different landscapes in multi-dictionary learning to help dictionaries capture the distinctions between regions. The Bayesian framework makes full use of the priori information while the input image is classified. The experiments with one simulated dataset and two satellite datasets validate that the MDBFM is highly effective in both subjective and objective evaluation indexes. The results of MDBFM show more precise details and have a higher similarity with real images when dealing with both type changes and phenology changes.<\/jats:p>","DOI":"10.3390\/ijgi6110374","type":"journal-article","created":{"date-parts":[[2017,11,21]],"date-time":"2017-11-21T11:23:20Z","timestamp":1511263400000},"page":"374","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Spatio-Temporal Series Remote Sensing Image Prediction Based on Multi-Dictionary Bayesian Fusion"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4947-662X","authenticated-orcid":false,"given":"Chu","family":"He","sequence":"first","affiliation":[{"name":"Electronic and Information School, Wuhan University, Wuhan 430072, China"},{"name":"State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"given":"Zhi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Electronic and Information School, Wuhan University, Wuhan 430072, China"}]},{"given":"Dehui","family":"Xiong","sequence":"additional","affiliation":[{"name":"Electronic and Information School, Wuhan University, Wuhan 430072, China"}]},{"given":"Juan","family":"Du","sequence":"additional","affiliation":[{"name":"Remote Sensing and Information Engineering School, Wuhan University, Wuhan 430079, China"}]},{"given":"Mingsheng","family":"Liao","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,11,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7847","DOI":"10.1080\/01431161.2010.531783","article-title":"Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso, Brazil","volume":"32","author":"Arvor","year":"2011","journal-title":"Int. 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