{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T23:55:26Z","timestamp":1780703726850,"version":"3.54.1"},"reference-count":66,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T00:00:00Z","timestamp":1714348800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific research project of Wuhan Polytechnic University","award":["2023RZ025"],"award-info":[{"award-number":["2023RZ025"]}]},{"name":"Scientific research project of Wuhan Polytechnic University","award":["2019-KZ-01"],"award-info":[{"award-number":["2019-KZ-01"]}]},{"name":"Key Laboratory of the Northern Qinghai\u2013Tibet Plateau Geological Processes and Mineral Resources","award":["2023RZ025"],"award-info":[{"award-number":["2023RZ025"]}]},{"name":"Key Laboratory of the Northern Qinghai\u2013Tibet Plateau Geological Processes and Mineral Resources","award":["2019-KZ-01"],"award-info":[{"award-number":["2019-KZ-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Obtaining accurate and real-time spatial distribution information regarding crops is critical for enabling effective smart agricultural management. In this study, innovative decision fusion strategies, including Enhanced Overall Accuracy Index (E-OAI) voting and the Overall Accuracy Index-based Majority Voting (OAI-MV), were introduced to optimize the use of diverse remote sensing data and various classifiers, thereby improving the accuracy of crop\/vegetation identification. These strategies were utilized to integrate crop\/vegetation classification outcomes from distinct feature sets (including Gaofen-6 reflectance, Sentinel-2 time series of vegetation indices, Sentinel-2 time series of biophysical variables, Sentinel-1 time series of backscatter coefficients, and their combinations) using distinct classifiers (Random Forests (RFs), Support Vector Machines (SVMs), Maximum Likelihood (ML), and U-Net), taking two grain-producing areas (Site #1 and Site #2) in Haixi Prefecture, Qinghai Province, China, as the research area. The results indicate that employing U-Net on feature-combined sets yielded the highest overall accuracy (OA) of 81.23% and 91.49% for Site #1 and Site #2, respectively, in the single classifier experiments. The E-OAI strategy, compared to the original OAI strategy, boosted the OA by 0.17% to 6.28%. Furthermore, the OAI-MV strategy achieved the highest OA of 86.02% and 95.67% for the respective study sites. This study highlights the distinct strengths of various remote sensing features and classifiers in discerning different crop and vegetation types. Additionally, the proposed OAI-MV and E-OAI strategies effectively harness the benefits of diverse classifiers and multisource remote sensing features, significantly enhancing the accuracy of crop\/vegetation classification.<\/jats:p>","DOI":"10.3390\/rs16091579","type":"journal-article","created":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T06:13:22Z","timestamp":1714371202000},"page":"1579","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Innovative Decision Fusion for Accurate Crop\/Vegetation Classification with Multiple Classifiers and Multisource Remote Sensing Data"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-6100-0279","authenticated-orcid":false,"given":"Shuang","family":"Shuai","sequence":"first","affiliation":[{"name":"School of Civil Engineering and Architecture, Wuhan Polytechnic University, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geophysics and Geomatics, China University of Geoscience (Wuhan), Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Zhang","sequence":"additional","affiliation":[{"name":"China Communications Construction Company Second Highway Consultants Limited Company, Wuhan 430056, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Management, Wuhan Polytechnic University, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Management, Wuhan Polytechnic University, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Duan","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Architecture, Wuhan Polytechnic University, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1069-9372","authenticated-orcid":false,"given":"Jie","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Architecture, Wuhan Polytechnic University, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,29]]},"reference":[{"key":"ref_1","unstructured":"Alexandratos, N. 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