{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T16:16:44Z","timestamp":1776183404621,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T00:00:00Z","timestamp":1658880000000},"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":["62001129"],"award-info":[{"award-number":["62001129"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021GXNSFBA075029"],"award-info":[{"award-number":["2021GXNSFBA075029"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004607","name":"Guangxi Natural Science Foundation","doi-asserted-by":"publisher","award":["62001129"],"award-info":[{"award-number":["62001129"]}],"id":[{"id":"10.13039\/501100004607","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004607","name":"Guangxi Natural Science Foundation","doi-asserted-by":"publisher","award":["2021GXNSFBA075029"],"award-info":[{"award-number":["2021GXNSFBA075029"]}],"id":[{"id":"10.13039\/501100004607","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The selection and representation of classification features in remote sensing image play crucial roles in image classification accuracy. To effectively improve the features classification accuracy, an improved U-Net remote sensing classification algorithm fusing attention and multiscale features is proposed in this paper, called spatial attention-atrous spatial pyramid pooling U-Net (SA-UNet). This framework connects atrous spatial pyramid pooling (ASPP) with the convolutional units of the encoder of the original U-Net in the form of residuals. The ASPP module expands the receptive field, integrates multiscale features in the network, and enhances the ability to express shallow features. Through the fusion residual module, shallow and deep features are deeply fused, and the characteristics of shallow and deep features are further used. The spatial attention mechanism is used to combine spatial with semantic information so that the decoder can recover more spatial information. In this study, the crop distribution in central Guangxi province was analyzed, and experiments were conducted based on Landsat 8 multispectral remote sensing images. The experimental results showed that the improved algorithm increases the classification accuracy, with the accuracy increasing from 93.33% to 96.25%, The segmentation accuracy of sugarcane, rice, and other land increased from 96.42%, 63.37%, and 88.43% to 98.01%, 83.21%, and 95.71%, respectively. The agricultural planting area results obtained by the proposed algorithm can be used as input data for regional ecological models, which is conducive to the development of accurate and real-time crop growth change models.<\/jats:p>","DOI":"10.3390\/rs14153591","type":"journal-article","created":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T03:21:16Z","timestamp":1658978476000},"page":"3591","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Improved U-Net Remote Sensing Classification Algorithm Fusing Attention and Multiscale Features"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1685-4989","authenticated-orcid":false,"given":"Xiangsuo","family":"Fan","sequence":"first","affiliation":[{"name":"School of Automation, Guangxi University of Science and Technology, Liuzhou 545006, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5564-727X","authenticated-orcid":false,"given":"Chuan","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Automation, Guangxi University of Science and Technology, Liuzhou 545006, China"}]},{"given":"Jinlong","family":"Fan","sequence":"additional","affiliation":[{"name":"National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China"}]},{"given":"Nayi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automation, Guangxi University of Science and Technology, Liuzhou 545006, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112365","DOI":"10.1016\/j.rse.2021.112365","article-title":"Integrating coarse-resolution images and agricultural statistics to generate sub-pixel crop type maps and reconciled area estimates","volume":"258","author":"Hu","year":"2021","journal-title":"Remote Sens. 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