{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T15:46:25Z","timestamp":1774367185651,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,21]],"date-time":"2024-01-21T00:00:00Z","timestamp":1705795200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Innovation Fund of Marine Defense Technology Innovation Center of China: 2022 Innovation Center Innovation Fund Project","award":["JJ-2022-712-02"],"award-info":[{"award-number":["JJ-2022-712-02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The present study proposes a novel deep-learning model for remote sensing image enhancement. It maintains image details while enhancing brightness in the feature extraction module. An improved hierarchical model named Global Spatial Attention Network (GSA-Net), based on U-Net for image enhancement, is proposed to improve the model\u2019s performance. To circumvent the issue of insufficient sample data, gamma correction is applied to create low-light images, which are then used as training examples. A loss function is constructed using the Structural Similarity (SSIM) and Peak Signal-to-Noise Ratio (PSNR) indices. The GSA-Net network and loss function are utilized to restore images obtained via low-light remote sensing. This proposed method was tested on the Northwestern Polytechnical University Very-High-Resolution 10 (NWPU VHR-10) dataset, and its overall superiority was demonstrated in comparison with other state-of-the-art algorithms using various objective assessment indicators, such as PSNR, SSIM, and Learned Perceptual Image Patch Similarity (LPIPS). Furthermore, in high-level visual tasks such as object detection, this novel method provides better remote sensing images with distinct details and higher contrast than the competing methods.<\/jats:p>","DOI":"10.3390\/s24020673","type":"journal-article","created":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T11:36:41Z","timestamp":1705923401000},"page":"673","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Deep Learning-Based Technique for Remote Sensing Image Enhancement Using Multiscale Feature Fusion"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1647-1769","authenticated-orcid":false,"given":"Ming","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Computer Science, Yangtze University, Jingzhou 434023, China"}]},{"given":"Rui","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Yangtze University, Jingzhou 434023, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4032-5546","authenticated-orcid":false,"given":"Min","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Yangtze University, Jingzhou 434023, China"}]},{"given":"Botao","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Yangtze University, Jingzhou 434023, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1109\/LGRS.2009.2034873","article-title":"Satellite image contrast enhancement using discrete wavelet transform and singular value decomposition","volume":"7","author":"Demirel","year":"2010","journal-title":"IEEE Geosci. 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