{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T01:50:28Z","timestamp":1776131428284,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T00:00:00Z","timestamp":1660089600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key R&amp;D project of Sichuan Province","award":["2022YFG0153"],"award-info":[{"award-number":["2022YFG0153"]}]},{"name":"Key R&amp;D project of Sichuan Province","award":["GSJDJS2021010"],"award-info":[{"award-number":["GSJDJS2021010"]}]},{"name":"Key R&amp;D project of Sichuan Province","award":["2020SCUNG205"],"award-info":[{"award-number":["2020SCUNG205"]}]},{"name":"Sichuan University","award":["2022YFG0153"],"award-info":[{"award-number":["2022YFG0153"]}]},{"name":"Sichuan University","award":["GSJDJS2021010"],"award-info":[{"award-number":["GSJDJS2021010"]}]},{"name":"Sichuan University","award":["2020SCUNG205"],"award-info":[{"award-number":["2020SCUNG205"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The accurate spatial positioning of the target in a fixed camera image is a critical sensing technique. Conventional visual spatial positioning methods rely on tedious camera calibration and face great challenges in selecting the representative feature points to compute the position of the target, especially when existing occlusion or in remote scenes. In order to avoid these deficiencies, this paper proposes a deep learning approach for accurate visual spatial positioning of the targets with the assistance of Global Navigation Satellite System (GNSS). It contains two stages: the first stage trains a hybrid supervised and unsupervised auto-encoder regression network offline to gain capability of regressing geolocation (longitude and latitude) directly from the fusion of image and GNSS, and learns an error scale factor to evaluate the regression error. The second stage firstly predicts regressed accurate geolocation online from the observed image and GNSS measurement, and then filters the predictive geolocation and the measured GNSS to output the optimal geolocation. The experimental results showed that the proposed approach increased the average positioning accuracy by 56.83%, 37.25%, 41.62% in a simulated scenario and 31.25%, 7.43%, 38.28% in a real-world scenario, compared with GNSS, the Interacting Multiple Model\u2212Unscented Kalman Filters (IMM-UKF) and the supervised deep learning approach, respectively. Other improvements were also achieved in positioning stability, robustness, generalization, and performance in GNSS denied environments.<\/jats:p>","DOI":"10.3390\/rs14163877","type":"journal-article","created":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T09:42:56Z","timestamp":1660124576000},"page":"3877","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Accurate Spatial Positioning of Target Based on the Fusion of Uncalibrated Image and GNSS"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7402-668X","authenticated-orcid":false,"given":"Binbin","family":"Liang","sequence":"first","affiliation":[{"name":"School of Aeronautics and Astronautics, Sichuan University, Chengdu 610065, China"}]},{"given":"Songchen","family":"Han","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, Sichuan University, Chengdu 610065, China"}]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, Sichuan University, Chengdu 610065, China"}]},{"given":"Daoyong","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, Sichuan University, Chengdu 610065, China"}]},{"given":"Ruliang","family":"He","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, Sichuan University, Chengdu 610065, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4189-1746","authenticated-orcid":false,"given":"Guoxin","family":"Huang","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu 610065, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1109\/34.888718","article-title":"A flexible new technique for camera calibration","volume":"22","author":"Zhang","year":"2000","journal-title":"IEEE Trans. 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