{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:25:23Z","timestamp":1760235923645,"version":"build-2065373602"},"reference-count":76,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,10,6]],"date-time":"2021-10-06T00:00:00Z","timestamp":1633478400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41701399 and 42061064"],"award-info":[{"award-number":["41701399 and 42061064"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Satellite Image Time Series (SITS) have become more accessible in recent years and SITS analysis has attracted increasing research interest. Given that labeled SITS training samples are time and effort consuming to acquire, clustering or unsupervised analysis methods need to be developed. Similarity measure is critical for clustering, however, currently established methods represented by Dynamic Time Warping (DTW) still exhibit several issues when coping with SITS, such as pathological alignment, sensitivity to spike noise, and limitation on capacity. In this paper, we introduce a new time series similarity measure method named time adaptive optimal transport (TAOT) to the application of SITS clustering. TAOT inherits several promising properties of optimal transport for the comparing of time series. Statistical and visual results on two real SITS datasets with two different settings demonstrate that TAOT can effectively alleviate the issues of DTW and further improve the clustering accuracy. Thus, TAOT can serve as a usable tool to explore the potential of precious SITS data.<\/jats:p>","DOI":"10.3390\/rs13193993","type":"journal-article","created":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T21:26:20Z","timestamp":1633728380000},"page":"3993","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Satellite Image Time Series Clustering via Time Adaptive Optimal Transport"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4549-3502","authenticated-orcid":false,"given":"Zheng","family":"Zhang","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Tang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weixiong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Marine Information Engineering, Hainan Tropical Ocean University, Hainan 572022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3081","DOI":"10.1109\/TGRS.2011.2179050","article-title":"Satellite image time series analysis under time warping","volume":"50","author":"Petitjean","year":"2012","journal-title":"IEEE Trans. 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