{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T04:22:47Z","timestamp":1777004567201,"version":"3.51.4"},"reference-count":70,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,5,1]],"date-time":"2020-05-01T00:00:00Z","timestamp":1588291200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"This research was partially supported by Alberta Geological Survey (AGS) and NSERC discovery grant","award":["No grant number."],"award-info":[{"award-number":["No grant number."]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The detection performance of small objects in remote sensing images has not been satisfactory compared to large objects, especially in low-resolution and noisy images. A generative adversarial network (GAN)-based model called enhanced super-resolution GAN (ESRGAN) showed remarkable image enhancement performance, but reconstructed images usually miss high-frequency edge information. Therefore, object detection performance showed degradation for small objects on recovered noisy and low-resolution remote sensing images. Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we applied a new edge-enhanced super-resolution GAN (EESRGAN) to improve the quality of remote sensing images and used different detector networks in an end-to-end manner where detector loss was backpropagated into the EESRGAN to improve the detection performance. We proposed an architecture with three components: ESRGAN, EEN, and Detection network. We used residual-in-residual dense blocks (RRDB) for both the ESRGAN and EEN, and for the detector network, we used a faster region-based convolutional network (FRCNN) (two-stage detector) and a single-shot multibox detector (SSD) (one stage detector). Extensive experiments on a public (car overhead with context) dataset and another self-assembled (oil and gas storage tank) satellite dataset showed superior performance of our method compared to the standalone state-of-the-art object detectors.<\/jats:p>","DOI":"10.3390\/rs12091432","type":"journal-article","created":{"date-parts":[[2020,5,4]],"date-time":"2020-05-04T14:00:43Z","timestamp":1588600843000},"page":"1432","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":255,"title":["Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9572-9010","authenticated-orcid":false,"given":"Jakaria","family":"Rabbi","sequence":"first","affiliation":[{"name":"Department of Computing Science, 2-32 Athabasca Hall, University of Alberta, Edmonton, AB T6G 2E8, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7588-5400","authenticated-orcid":false,"given":"Nilanjan","family":"Ray","sequence":"additional","affiliation":[{"name":"Department of Computing Science, 2-32 Athabasca Hall, University of Alberta, Edmonton, AB T6G 2E8, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6566-6343","authenticated-orcid":false,"given":"Matthias","family":"Schubert","sequence":"additional","affiliation":[{"name":"Institute for Informatic, Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen, Oettingenstra\u00dfe 67, D-80333 Munich, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2410-5571","authenticated-orcid":false,"given":"Subir","family":"Chowdhury","sequence":"additional","affiliation":[{"name":"Alberta Geological Survey, Alberta Energy Regulator, Edmonton, AB T6B 2X3, Canada"}]},{"given":"Dennis","family":"Chao","sequence":"additional","affiliation":[{"name":"Alberta Geological Survey, Alberta Energy Regulator, Edmonton, AB T6B 2X3, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.isprsjprs.2014.02.013","article-title":"Unmanned aerial systems for photogrammetry and remote sensing: A review","volume":"92","author":"Colomina","year":"2014","journal-title":"ISPRS J. 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