{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T19:14:24Z","timestamp":1769368464693,"version":"3.49.0"},"reference-count":47,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,7,29]],"date-time":"2021-07-29T00:00:00Z","timestamp":1627516800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFC0407105"],"award-info":[{"award-number":["2018YFC0407105"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFC0407905"],"award-info":[{"award-number":["2018YFC0407905"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFC0405505"],"award-info":[{"award-number":["2017YFC0405505"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Technology Project of China Huaneng Group","award":["MW 2017\/P28"],"award-info":[{"award-number":["MW 2017\/P28"]}]},{"name":"National Natural Science Fund of China","award":["51779100"],"award-info":[{"award-number":["51779100"]}]},{"name":"National Natural Science Fund of China","award":["51679103"],"award-info":[{"award-number":["51679103"]}]},{"name":"Central Public-interest Scientific Institution Basal Research Fund","award":["HKY-JBYW-2020-21"],"award-info":[{"award-number":["HKY-JBYW-2020-21"]}]},{"name":"Central Public-interest Scientific Institution Basal Research Fund","award":["HKY-JBYW-2020-07"],"award-info":[{"award-number":["HKY-JBYW-2020-07"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Semantic segmentation of remote sensing imagery is a fundamental task in intelligent interpretation. Since deep convolutional neural networks (DCNNs) performed considerable insight in learning implicit representations from data, numerous works in recent years have transferred the DCNN-based model to remote sensing data analysis. However, the wide-range observation areas, complex and diverse objects and illumination and imaging angle influence the pixels easily confused, leading to undesirable results. Therefore, a remote sensing imagery semantic segmentation neural network, named HCANet, is proposed to generate representative and discriminative representations for dense predictions. HCANet hybridizes cross-level contextual and attentive representations to emphasize the distinguishability of learned features. First of all, a cross-level contextual representation module (CCRM) is devised to exploit and harness the superpixel contextual information. Moreover, a hybrid representation enhancement module (HREM) is designed to fuse cross-level contextual and self-attentive representations flexibly. Furthermore, the decoder incorporates DUpsampling operation to boost the efficiency losslessly. The extensive experiments are implemented on the Vaihingen and Potsdam benchmarks. In addition, the results indicate that HCANet achieves excellent performance on overall accuracy and mean intersection over union. In addition, the ablation study further verifies the superiority of CCRM.<\/jats:p>","DOI":"10.3390\/rs13152986","type":"journal-article","created":{"date-parts":[[2021,7,29]],"date-time":"2021-07-29T21:21:21Z","timestamp":1627593681000},"page":"2986","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Hybridizing Cross-Level Contextual and Attentive Representations for Remote Sensing Imagery Semantic Segmentation"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0576-3181","authenticated-orcid":false,"given":"Xin","family":"Li","sequence":"first","affiliation":[{"name":"College of Computer and Information, Hohai University, Nanjing 211100, China"},{"name":"Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing 211100, China"},{"name":"Information Engineering Center, Yellow River Institute of Hydraulic Research, Zhengzhou 450003, China"}]},{"given":"Feng","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Computer and Information, Hohai University, Nanjing 211100, China"},{"name":"Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing 211100, China"}]},{"given":"Runliang","family":"Xia","sequence":"additional","affiliation":[{"name":"Information Engineering Center, Yellow River Institute of Hydraulic Research, Zhengzhou 450003, China"}]},{"given":"Xin","family":"Lyu","sequence":"additional","affiliation":[{"name":"College of Computer and Information, Hohai University, Nanjing 211100, China"},{"name":"Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing 211100, China"}]},{"given":"Hongmin","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Computer and Information, Hohai University, Nanjing 211100, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7885-3922","authenticated-orcid":false,"given":"Yao","family":"Tong","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep learning for remote sensing data: A technical tutorial on the state of the art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. 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