{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T22:27:59Z","timestamp":1772836079009,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,12,3]],"date-time":"2021-12-03T00:00:00Z","timestamp":1638489600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China under grant","award":["41971313"],"award-info":[{"award-number":["41971313"]}]},{"name":"Natural Science Foundation of Hunan Province under grant","award":["2019JJ40372"],"award-info":[{"award-number":["2019JJ40372"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>To solve the problems of susceptibility to image noise, subjectivity of training sample selection, and inefficiency of state-of-the-art change detection methods with heterogeneous images, this study proposes a post-classification change detection method for heterogeneous images with improved training of hierarchical extreme learning machine (HELM). After smoothing the images to suppress noise, a sample selection method is defined to train the HELM for each image, in which the feature extraction is respectively implemented for heterogeneous images and the parameters need not be fine-tuned. Then, the multi-temporal feature maps extracted from the trained HELM are segmented to obtain classification maps and then compared to generate a change map with changed types. The proposed method is validated experimentally by using one set of synthetic aperture radar (SAR) images obtained from Sentinel-1, one set of optical images acquired from Google Earth, and two sets of heterogeneous SAR and optical images. The results show that compared to state-of-the-art change detection methods, the proposed method can improve the accuracy of change detection by more than 8% in terms of the kappa coefficient and greatly reduce run time regardless of the type of images used. Such enhancement reflects the robustness and superiority of the proposed method.<\/jats:p>","DOI":"10.3390\/rs13234918","type":"journal-article","created":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T03:10:38Z","timestamp":1638760238000},"page":"4918","update-policy":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Change Detection for Heterogeneous Remote Sensing Images with Improved Training of Hierarchical Extreme Learning Machine (HELM)"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0001-8973-3363","authenticated-orcid":false,"given":"Te","family":"Han","sequence":"first","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0001-5578-4330","authenticated-orcid":false,"given":"Yuqi","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"},{"name":"Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University), Ministry of Education, Changsha 410083, China"},{"name":"Key Laboratory of Spatio-Temporal Information and Intelligent Services, Ministry of Natural Resources, Changsha 410083, China"}]},{"given":"Xin","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}]},{"given":"Zefeng","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}]},{"given":"Bin","family":"Zou","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"},{"name":"Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University), Ministry of Education, Changsha 410083, China"},{"name":"Key Laboratory of Spatio-Temporal Information and Intelligent Services, Ministry of Natural Resources, Changsha 410083, China"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0003-1535-9940","authenticated-orcid":false,"given":"Huihui","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"},{"name":"Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University), Ministry of Education, Changsha 410083, China"},{"name":"Key Laboratory of Spatio-Temporal Information and Intelligent Services, Ministry of Natural Resources, Changsha 410083, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1080\/0143116031000139863","article-title":"Change detection techniques","volume":"25","author":"Lu","year":"2004","journal-title":"Int. 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