{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T04:19:55Z","timestamp":1778818795394,"version":"3.51.4"},"reference-count":41,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,20]],"date-time":"2021-08-20T00:00:00Z","timestamp":1629417600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent years, research on increasing the spatial resolution and enhancing the quality of satellite images using the deep learning-based super-resolution (SR) method has been actively conducted. In a remote sensing field, conventional SR methods required high-quality satellite images as the ground truth. However, in most cases, high-quality satellite images are difficult to acquire because many image distortions occur owing to various imaging conditions. To address this problem, we propose an adaptive image quality modification method to improve SR image quality for the KOrea Multi-Purpose Satellite-3 (KOMPSAT-3). The KOMPSAT-3 is a high performance optical satellite, which provides 0.7-m ground sampling distance (GSD) panchromatic and 2.8-m GSD multi-spectral images for various applications. We proposed an SR method with a scale factor of 2 for the panchromatic and pan-sharpened images of KOMPSAT-3. The proposed SR method presents a degradation model that generates a low-quality image for training, and a method for improving the quality of the raw satellite image. The proposed degradation model for low-resolution input image generation is based on Gaussian noise and blur kernel. In addition, top-hat and bottom-hat transformation is applied to the original satellite image to generate an enhanced satellite image with improved edge sharpness or image clarity. Using this enhanced satellite image as the ground truth, an SR network is then trained. The performance of the proposed method was evaluated by comparing it with other SR methods in multiple ways, such as edge extraction, visual inspection, qualitative analysis, and the performance of object detection. Experimental results show that the proposed SR method achieves improved reconstruction results and perceptual quality compared to conventional SR methods.<\/jats:p>","DOI":"10.3390\/rs13163301","type":"journal-article","created":{"date-parts":[[2021,8,22]],"date-time":"2021-08-22T22:59:27Z","timestamp":1629673167000},"page":"3301","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A No-Reference CNN-Based Super-Resolution Method for KOMPSAT-3 Using Adaptive Image Quality Modification"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8994-4128","authenticated-orcid":false,"given":"Yeonju","family":"Choi","sequence":"first","affiliation":[{"name":"Korea Aerospace Research Institute, 169-84 Gwahak-ro, Yuseong-Gu, Daejeon 34133, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3724-1273","authenticated-orcid":false,"given":"Sanghyuck","family":"Han","sequence":"additional","affiliation":[{"name":"Korea Aerospace Research Institute, 169-84 Gwahak-ro, Yuseong-Gu, Daejeon 34133, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1011-2319","authenticated-orcid":false,"given":"Yongwoo","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of System Semiconductor Engineering, Sangmyung University, Cheonan 31066, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image super-resolution using deep convolutional networks","volume":"38","author":"Dong","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., and Lee, K.M. (2016, January 27\u201330). Accurate image super-resolution using very deep convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.182"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., and Lee, K.M. (2016, January 27\u201330). Deeply-recursive convolutional network for image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.181"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., and Mu Lee, K. (2017, January 21\u201326). Enhanced deep residual networks for single image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.151"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Tai, Y., Yang, J., and Liu, X. (2017, January 21\u201326). Image super-resolution via deep recursive residual network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.298"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Haris, M., Shakhnarovich, G., and Ukita, N. (2018, January 18\u201322). Deep back-projection networks for super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00179"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., and Fu, Y. (2018, January 18\u201322). Residual dense network for image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00262"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., and Fu, Y. (2018, January 8\u201314). Image super-resolution using very deep residual channel attention networks. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., and Wang, Z. (2017, January 21\u201326). Photo-realistic single image super-resolution using a generative adversarial network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Sajjadi, M.S., Scholkopf, B., and Hirsch, M. (2017, January 21\u201326). Enhancenet: Single image super-resolution through automated texture synthesis. Proceedings of the IEEE International Conference on Computer Vision, Honolulu, HI, USA.","DOI":"10.1109\/ICCV.2017.481"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tan, W., Yan, B., and Bare, B. (2018, January 18\u201322). Feature super-resolution: Make machine see more clearly. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00420"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y., and Change Loy, C. (2018, January 8\u201314). Esrgan: Enhanced super-resolution generative adversarial networks. Proceedings of the European Conference on Computer Vision (ECCV) Workshops, Munich, Germany.","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"ref_13","unstructured":"Lucas, A., Lopez-Tapia, S., Molina, R., and Katsaggelos, A.K. (2019). Self-supervised Fine-tuning for Correcting Super-Resolution Convolutional Neural Networks. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Efrat, N., Glasner, D., Apartsin, A., Nadler, B., and Levin, A. (2013, January 1\u20138). Accurate blur models vs. image priors in single image super-resolution. Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.352"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Michaeli, T., and Irani, M. (2013, January 1\u20138). Nonparametric Blind Super-resolution. Proceedings of the 2013 IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.121"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"012048","DOI":"10.1088\/1742-6596\/124\/1\/012048","article-title":"Simultaneous super-resolution and blind deconvolution","volume":"124","author":"Sroubek","year":"2008","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., and Zhang, L. (2019, January 15\u201320). Deep plug-and-play super-resolution for arbitrary blur kernels. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00177"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, K., Van Gool, L., and Timofte, R. (2020, January 14\u201319). Deep Unfolding Network for Image Super-Resolution. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00328"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, L., Wang, Y., Dong, X., Xu, Q., Yang, J., An, W., and Guo, Y. (2021, January 19\u201325). Unsupervised Degradation Representation Learning for Blind Super-Resolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Virtual.","DOI":"10.1109\/CVPR46437.2021.01044"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kim, S.Y., Sim, H., and Kim, M. (2021, January 19\u201325). KOALAnet: Blind Super-Resolution Using Kernel-Oriented Adaptive Local Adjustment. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Virtual.","DOI":"10.1109\/CVPR46437.2021.01047"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.jvcir.2018.02.016","article-title":"Single satellite imagery simultaneous super-resolution and colorization using multi-task deep neural networks","volume":"53","author":"Liu","year":"2018","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jiang, K., Wang, Z., Yi, P., Jiang, J., Xiao, J., and Yao, Y. (2018). Deep Distillation Recursive Network for Remote Sensing Imagery Super-Resolution. Remote Sens., 10.","DOI":"10.3390\/rs10111700"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kwan, C. (2018). Remote Sensing Performance Enhancement in Hyperspectral Images. Sensors, 18.","DOI":"10.3390\/s18113598"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Burdziakowski, P. (2020). Increasing the Geometrical and Interpretation Quality of Unmanned Aerial Vehicle Photogrammetry Products using Super-Resolution Algorithms. Remote Sens., 12.","DOI":"10.3390\/rs12050810"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4047","DOI":"10.1109\/TGRS.2017.2687419","article-title":"Superresolution for UAV Images via Adaptive Multiple Sparse Representation and Its Application to 3-D Reconstruction","volume":"55","author":"Haris","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"12053","DOI":"10.3390\/s150512053","article-title":"Multisensor Super Resolution Using Directionally-Adaptive Regularization for UAV Images","volume":"15","author":"Kang","year":"2015","journal-title":"Sensors"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.neucom.2019.03.106","article-title":"Ultra-dense GAN for satellite imagery super-resolution","volume":"398","author":"Wang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Bosch, M., Gifford, C.M., and Rodriguez, P.A. (2018, January 12\u201315). Super-resolution for overhead imagery using densenets and adversarial learning. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00159"},{"key":"ref_29","first-page":"883","article-title":"Single-image super resolution for multispectral remote sensing data using convolutional neural networks","volume":"41","author":"Liebel","year":"2016","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Shermeyer, J., and Van Etten, A. (2019, January 15\u201320). The effects of super-resolution on object detection performance in satellite imagery. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00184"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Rabbi, J., Ray, N., Schubert, M., Chowdhury, S., and Chao, D. (2020). Small-object detection in remote sensing images with end-to-end edge-enhanced GAN and object detector network. Remote Sens., 12.","DOI":"10.20944\/preprints202003.0313.v2"},{"key":"ref_32","unstructured":"Zhou, R., and Susstrunk, S. (November, January 27). Kernel modeling super-resolution on real low-resolution images. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1093\/biomet\/81.3.425","article-title":"Ideal spatial adaptation by wavelet shrinkage","volume":"81","author":"Donoho","year":"1994","journal-title":"Biometrika"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1016\/j.ejrnm.2015.01.004","article-title":"Using morphological transforms to enhance the contrast of medical images","volume":"46","author":"Hassanpour","year":"2015","journal-title":"Egypt. J. Radiol. Nucl. Med."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1109\/TPAMI.1987.4767941","article-title":"Image analysis using mathematical morphology","volume":"PAMI-9","author":"Haralick","year":"1987","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.1109\/TPAMI.2012.213","article-title":"Guided image filtering","volume":"35","author":"He","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.optlastec.2011.07.009","article-title":"Image enhancement using multi scale image features extracted by top-hat transform","volume":"44","author":"Bai","year":"2012","journal-title":"Opt. Laser Technol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/LSP.2012.2227726","article-title":"Making a \u201ccompletely blind\u201d image quality analyzer","volume":"20","author":"Mittal","year":"2012","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_39","unstructured":"(2021, June 10). USGS Remote Sensing Technologies Test Sites, Available online: https:\/\/calval.cr.usgs.gov\/apps\/test_sites_catalog."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.5194\/isprsarchives-XL-7-W3-1233-2015","article-title":"Comprehensive calibration and validation site for information remote sensing","volume":"40","author":"Li","year":"2015","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Han, J., Ding, J., Li, J., and Xia, G.S. (2021). Align deep features for oriented object detection. IEEE Trans. Geosci. Remote Sens., 1\u201311.","DOI":"10.1109\/TGRS.2021.3062048"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3301\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:48:18Z","timestamp":1760165298000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3301"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,20]]},"references-count":41,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13163301"],"URL":"https:\/\/doi.org\/10.3390\/rs13163301","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,20]]}}}