{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T18:22:26Z","timestamp":1776363746987,"version":"3.51.2"},"reference-count":61,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2019,12,5]],"date-time":"2019-12-05T00:00:00Z","timestamp":1575504000000},"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":["41801327, 41601405"],"award-info":[{"award-number":["41801327, 41601405"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The Priority Academic Program Development of Jiangsu Higher Education Institutions","award":["No."],"award-info":[{"award-number":["No."]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Detailed land use and land cover (LULC) information is one of the important information for land use surveys and applications related to the earth sciences. Therefore, LULC classification using very-high resolution remotely sensed imagery has been a hot issue in the remote sensing community. However, it remains a challenge to successfully extract LULC information from very-high resolution remotely sensed imagery, due to the difficulties in describing the individual characteristics of various LULC categories using single level features. The traditional pixel-wise or spectral-spatial based methods pay more attention to low-level feature representations of target LULC categories. In addition, deep convolutional neural networks offer great potential to extract high-level features to describe objects and have been successfully applied to scene understanding or classification. However, existing studies has paid little attention to constructing multi-level feature representations to better understand each category. In this paper, a multi-level feature representation framework is first designed to extract more robust feature representations for the complex LULC classification task using very-high resolution remotely sensed imagery. To this end, spectral reflection and morphological and morphological attribute profiles are used to describe the pixel-level and neighborhood-level information. Furthermore, a novel object-based convolutional neural networks (CNN) is proposed to extract scene-level information. The object-based CNN method combines advantages of object-based method and CNN method and can perform multi-scale analysis at the scene level. Then, the random forest method is employed to carry out the final classification using the multi-level features. The proposed method was validated on three challenging remotely sensed imageries including a hyperspectral image and two multispectral images with very-high spatial resolution, and achieved excellent classification performances.<\/jats:p>","DOI":"10.3390\/rs11242916","type":"journal-article","created":{"date-parts":[[2019,12,5]],"date-time":"2019-12-05T11:16:31Z","timestamp":1575544591000},"page":"2916","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["High-Resolution Imagery Classification Based on Different Levels of Information"],"prefix":"10.3390","volume":"11","author":[{"given":"Erzhu","family":"Li","sequence":"first","affiliation":[{"name":"School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9091-6033","authenticated-orcid":false,"given":"Alim","family":"Samat","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8808-7961","authenticated-orcid":false,"given":"Wei","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cong","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuyu","family":"Bai","sequence":"additional","affiliation":[{"name":"School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2016.03.008","article-title":"Optical remotely sensed time series data for land cover classification: A review","volume":"116","author":"White","year":"2016","journal-title":"ISPRS J. 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