{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:18:35Z","timestamp":1775326715116,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,6,22]],"date-time":"2021-06-22T00:00:00Z","timestamp":1624320000000},"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":["61803061, 61906026"],"award-info":[{"award-number":["61803061, 61906026"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Chongqing Natural Science Foundation","award":["cstc2020jcyj-msxmX0577, cstc2020jcyj-msxmX0634"],"award-info":[{"award-number":["cstc2020jcyj-msxmX0577, cstc2020jcyj-msxmX0634"]}]},{"name":"&quot;Chengdu-Chongqing Economic Circle&quot; innovation funding of Chongqing Municipal Education Commission","award":["KJCXZD2020028"],"award-info":[{"award-number":["KJCXZD2020028"]}]},{"name":"Science and Technology Research Program of Chongqing Municipal Education Commission","award":["KJQN202000602"],"award-info":[{"award-number":["KJQN202000602"]}]},{"name":"Ministry of Education China Mobile Research Fund","award":["MCM 20180404"],"award-info":[{"award-number":["MCM 20180404"]}]},{"name":"Special key project of Chongqing technology innovation and application development","award":["cstc2019jscx-zdztzx0068"],"award-info":[{"award-number":["cstc2019jscx-zdztzx0068"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing images are widely used in object detection and tracking, military security, and other computer vision tasks. However, remote sensing images are often degraded by suspended aerosol in the air, especially under poor weather conditions, such as fog, haze, and mist. The quality of remote sensing images directly affect the normal operations of computer vision systems. As such, haze removal is a crucial and indispensable pre-processing step in remote sensing image processing. Additionally, most of the existing image dehazing methods are not applicable to all scenes, so the corresponding dehazed images may have varying degrees of color distortion. This paper proposes a novel atmospheric light estimation based dehazing algorithm to obtain high visual-quality remote sensing images. First, a differentiable function is used to train the parameters of a linear scene depth model for the scene depth map generation of remote sensing images. Second, the atmospheric light of each hazy remote sensing image is estimated by the corresponding scene depth map. Then, the corresponding transmission map is estimated on the basis of the estimated atmospheric light by a haze-lines model. Finally, according to the estimated atmospheric light and transmission map, an atmospheric scattering model is applied to remove haze from remote sensing images. The colors of the images dehazed by the proposed method are in line with the perception of human eyes in different scenes. A dataset with 100 remote sensing images from hazy scenes was built for testing. The performance of the proposed image dehazing method is confirmed by theoretical analysis and comparative experiments.<\/jats:p>","DOI":"10.3390\/rs13132432","type":"journal-article","created":{"date-parts":[[2021,6,22]],"date-time":"2021-06-22T22:10:59Z","timestamp":1624399859000},"page":"2432","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":78,"title":["Atmospheric Light Estimation Based Remote Sensing Image Dehazing"],"prefix":"10.3390","volume":"13","author":[{"given":"Zhiqin","family":"Zhu","sequence":"first","affiliation":[{"name":"College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Yaqin","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Hongyan","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Yong","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science, Chongqing University, Chongqing 400044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9562-3865","authenticated-orcid":false,"given":"Guanqiu","family":"Qi","sequence":"additional","affiliation":[{"name":"Computer Information Systems Department, State University of New York at Buffalo State, Buffalo, NY 14222, USA"}]},{"given":"Neal","family":"Mazur","sequence":"additional","affiliation":[{"name":"Computer Information Systems Department, State University of New York at Buffalo State, Buffalo, NY 14222, USA"}]},{"given":"Yuanyuan","family":"Li","sequence":"additional","affiliation":[{"name":"College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Penglong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"},{"name":"Chongqing Geomatics and Remote Sensing Center, Chongqing 401147, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1109\/TGRS.2012.2205158","article-title":"Geographic image retrieval using local invariant features","volume":"51","author":"Yang","year":"2012","journal-title":"IEEE Trans. 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