{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:44:56Z","timestamp":1775144696751,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T00:00:00Z","timestamp":1665360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2021YFB390110302"],"award-info":[{"award-number":["2021YFB390110302"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the development of deep learning, semantic segmentation technology has gradually become the mainstream technical method in large-scale multi-temporal landcover classification. Large-scale and multi-temporal are the two significant characteristics of Landsat imagery. However, the mainstream single-temporal semantic segmentation network lacks the constraints and assistance of pre-temporal information, resulting in unstable results, poor generalization ability, and inconsistency with the actual situation in the multi-temporal classification results. In this paper, we propose a multi-temporal network that introduces pre-temporal information as prior constrained auxiliary knowledge. We propose an element-wise weighting block module to improve the fine-grainedness of feature optimization. We propose a chained deduced classification strategy to improve multi-temporal classification\u2019s stability and generalization ability. We label the large-scale multi-temporal Landsat landcover classification dataset with an overall classification accuracy of over 90%. Through extensive experiments, compared with the mainstream semantic segmentation methods, our proposed multi-temporal network achieves state-of-the-art performance with good robustness and generalization ability.<\/jats:p>","DOI":"10.3390\/rs14195062","type":"journal-article","created":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T00:50:01Z","timestamp":1665449401000},"page":"5062","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Multi-Temporal Network for Improving Semantic Segmentation of Large-Scale Landsat Imagery"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2938-7419","authenticated-orcid":false,"given":"Xuan","family":"Yang","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0319-7753","authenticated-orcid":false,"given":"Bing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4293-6459","authenticated-orcid":false,"given":"Zhengchao","family":"Chen","sequence":"additional","affiliation":[{"name":"Airborne Remote Sensing Center, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2169-3577","authenticated-orcid":false,"given":"Yongqing","family":"Bai","sequence":"additional","affiliation":[{"name":"Airborne Remote Sensing Center, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5567-5801","authenticated-orcid":false,"given":"Pan","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.future.2014.10.029","article-title":"Remote sensing big data computing: Challenges and opportunities","volume":"51","author":"Ma","year":"2015","journal-title":"Future Gener. 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