{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:31:36Z","timestamp":1760239896331,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,1,11]],"date-time":"2019-01-11T00:00:00Z","timestamp":1547164800000},"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":["31670552"],"award-info":[{"award-number":["31670552"]}],"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>Hyperspectral images (HSIs) provide unique capabilities for urban impervious surfaces (UIS) extraction. This paper proposes a multi-feature extraction model (MFEM) for UIS detection from HSIs. The model is based on a nonlinear dimensionality reduction technique, t-distributed stochastic neighbor embedding (t-SNE), and the deep learning method convolutional deep belief networks (CDBNs). We improved the two methods to create a novel MFEM consisting of improved t-SNE, deep compression CDBNs (d-CDBNs), and a logistic regression classifier. The improved t-SNE method provides dimensionality reduction and spectral feature extraction from the original HSIs and the d-CDBNs algorithm extracts spatial feature and edges using the reduced dimensional datasets. Finally, the extracted features are combined into multi-feature for the impervious surface detection using the logistic regression classifier. After comparing with the commonly used methods, the current experimental results demonstrate that the proposed MFEM model provides better performance for UIS extraction and detection from HSIs.<\/jats:p>","DOI":"10.3390\/rs11020136","type":"journal-article","created":{"date-parts":[[2019,1,11]],"date-time":"2019-01-11T11:36:42Z","timestamp":1547206602000},"page":"136","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["An Improved Model Based Detection of Urban Impervious Surfaces Using Multiple Features Extracted from ROSIS-3 Hyperspectral Images"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3218-8146","authenticated-orcid":false,"given":"Yuliang","family":"Wang","sequence":"first","affiliation":[{"name":"College of Forestry, Nanjing Forestry University, Nanjing 210037, China"},{"name":"School of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, China"}]},{"given":"Huiyi","family":"Su","sequence":"additional","affiliation":[{"name":"College of Forestry, Nanjing Forestry University, Nanjing 210037, China"}]},{"given":"Mingshi","family":"Li","sequence":"additional","affiliation":[{"name":"College of Forestry, Nanjing Forestry University, Nanjing 210037, China"},{"name":"Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.rse.2011.02.030","article-title":"Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends","volume":"117","author":"Weng","year":"2012","journal-title":"Remote Sens. 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