{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T16:46:20Z","timestamp":1771951580276,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,26]],"date-time":"2021-12-26T00:00:00Z","timestamp":1640476800000},"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","doi-asserted-by":"publisher","award":["2018YFC0407905"],"award-info":[{"award-number":["2018YFC0407905"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science Fund for Distinguished Young Scholars of Henan Province","award":["202300410539"],"award-info":[{"award-number":["202300410539"]}]},{"name":"Science Fund for Excellent Young Scholars of Henan Province","award":["202148"],"award-info":[{"award-number":["202148"]}]},{"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 has been a fundamental task in interpreting remote sensing imagery (RSI) for various downstream applications. Due to the high intra-class variants and inter-class similarities, inflexibly transferring natural image-specific networks to RSI is inadvisable. To enhance the distinguishability of learnt representations, attention modules were developed and applied to RSI, resulting in satisfactory improvements. However, these designs capture contextual information by equally handling all the pixels regardless of whether they around edges. Therefore, blurry boundaries are generated, rising high uncertainties in classifying vast adjacent pixels. Hereby, we propose an edge distribution attention module (EDA) to highlight the edge distributions of leant feature maps in a self-attentive fashion. In this module, we first formulate and model column-wise and row-wise edge attention maps based on covariance matrix analysis. Furthermore, a hybrid attention module (HAM) that emphasizes the edge distributions and position-wise dependencies is devised combing with non-local block. Consequently, a conceptually end-to-end neural network, termed as EDENet, is proposed to integrate HAM hierarchically for the detailed strengthening of multi-level representations. EDENet implicitly learns representative and discriminative features, providing available and reasonable cues for dense prediction. The experimental results evaluated on ISPRS Vaihingen, Potsdam and DeepGlobe datasets show the efficacy and superiority to the state-of-the-art methods on overall accuracy (OA) and mean intersection over union (mIoU). In addition, the ablation study further validates the effects of EDA.<\/jats:p>","DOI":"10.3390\/rs14010102","type":"journal-article","created":{"date-parts":[[2021,12,27]],"date-time":"2021-12-27T01:06:54Z","timestamp":1640567214000},"page":"102","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Attentively Learning Edge Distributions for Semantic Segmentation of Remote Sensing Imagery"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0576-3181","authenticated-orcid":false,"given":"Xin","family":"Li","sequence":"first","affiliation":[{"name":"Information Engineering Center, Yellow River Institute of Hydraulic Research, Zhengzhou 450003, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5538-1865","authenticated-orcid":false,"given":"Tao","family":"Li","sequence":"additional","affiliation":[{"name":"Information Engineering Center, Yellow River Institute of Hydraulic Research, Zhengzhou 450003, China"}]},{"given":"Ziqi","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Earth System Science, Tsinghua University, Beijing 100084, China"}]},{"given":"Kaiwen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Dayu College, Hohai University, Nanjing 210024, China"}]},{"given":"Runliang","family":"Xia","sequence":"additional","affiliation":[{"name":"Information Engineering Center, Yellow River Institute of Hydraulic Research, Zhengzhou 450003, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2594","DOI":"10.1080\/01431161.2020.1856964","article-title":"DAU-Net: A novel water areas segmentation structure for remote sensing image","volume":"42","author":"Xia","year":"2021","journal-title":"Int. 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