{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T07:49:52Z","timestamp":1774424992941,"version":"3.50.1"},"reference-count":22,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,15]],"date-time":"2018-05-15T00:00:00Z","timestamp":1526342400000},"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":["41701527"],"award-info":[{"award-number":["41701527"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2017M612509"],"award-info":[{"award-number":["2017M612509"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Gaofen-4 is a geostationary orbit area array imaging satellite. Due to the difficulty of the on-orbit radiometric calibration of area array cameras, there is system noise in the images. This paper analyzes the source of the system noise, constructs a noise model of Gaofen-4, and proposes a practical method to remove the system noise using multiple images. Gaussian filtering is used to remove radiometric characteristics, and the Grubbs criterion is used to remove gradient characteristics, thereby transforming the images into noise images. System noise can be removed using correction coefficients obtained by superimposing multiple noise images. Using a variety of denoising methods to perform contrast experiments, the results show that the proposed method can effectively maintain image edge details and texture information while removing image noise.<\/jats:p>","DOI":"10.3390\/rs10050759","type":"journal-article","created":{"date-parts":[[2018,5,15]],"date-time":"2018-05-15T11:36:13Z","timestamp":1526384173000},"page":"759","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["System Noise Removal for Gaofen-4 Area-Array Camera"],"prefix":"10.3390","volume":"10","author":[{"given":"Xueli","family":"Chang","sequence":"first","affiliation":[{"name":"Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China"},{"name":"School of Resource and Environment Sciences, Wuhan University, Wuhan 430079, China"}]},{"given":"Luxiao","family":"He","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,15]]},"reference":[{"key":"ref_1","unstructured":"Mendenhall, J.A., Lencioni, D.E., and Evans, J.B. (2000). Earth Observing-1 Advanced Land Imager: Radiometric Response Calibration, Massachusetts Institute of Technology. MIT\/LL Project Report EO-1-3."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"389","DOI":"10.5194\/isprs-archives-XLI-B1-389-2016","article-title":"On-Orbit Geometric Calibration Approach for High-Resolution Geostationary Optical Satellite GaoFen-4","volume":"41","author":"Wang","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_3","unstructured":"Gonzalez, R.C., and Woods, R.E. (2005). Digital Image Processing, Prentice Hall International."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1137\/040616024","article-title":"A Review of Image Denoising Algorithms, with a New One","volume":"4","author":"Buades","year":"2005","journal-title":"Siam J. Multiscale Model. Simul."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2080","DOI":"10.1109\/TIP.2007.901238","article-title":"Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering","volume":"16","author":"Dabov","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"ref_6","unstructured":"Witkin, A.P. (1983, January 8\u201312). Scale-space filtering. Proceedings of the International Joint Conference on Artificial Intelligence, Karlsruhe, Germany."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1109\/34.56205","article-title":"Scale-Space and Edge Detection Using Anisotropic Diffusion","volume":"12","author":"Perona","year":"1990","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Weickert, J. (1996). Theoretical Foundations of Anisotropic Diffusion in Image Processing. Theoretical Foundations of Computer Vision, Springer.","DOI":"10.1007\/978-3-7091-6586-7_13"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"351","DOI":"10.7305\/automatika.2014.12.525","article-title":"An ICI Based Algorithm for Fast Denoising of Video Signals","volume":"55","author":"Lerga","year":"2014","journal-title":"Automatika"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Mandi\u0107, I., Pei\u0107, H., Lerga, J., and \u0160tajduhar, I. (2018). Denoising of X-ray Images Using the Adaptive Algorithm Based on the LPA-RICI Algorithm. J. Imaging, 4.","DOI":"10.3390\/jimaging4020034"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.automatica.2015.09.023","article-title":"An interpretation of the dual problem of the THREE-like approaches","volume":"62","author":"Zorzi","year":"2015","journal-title":"Automatica"},{"key":"ref_12","first-page":"823","article-title":"Multidimensional Rational Covariance Extension with Applications to Spectral Estimation and Image Compression","volume":"306","author":"Ringh","year":"2016","journal-title":"Mathematics"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/34.192463","article-title":"A Theory for Multiresolution Signal Decomposition: The Wavelet Representation","volume":"11","author":"Mallat","year":"1989","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1080\/01621459.1997.10473662","article-title":"Adaptive Bayesian Wavelet Shrinkage","volume":"92","author":"Chipman","year":"1997","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"909","DOI":"10.1109\/18.761332","article-title":"Analysis of multiresolution image denoising schemes using generalized Gaussian and complexity priors","volume":"45","author":"Moulin","year":"2015","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1056","DOI":"10.1109\/83.931100","article-title":"Bayesian tree-structured image modeling using wavelet-domain hidden Markov models","volume":"10","author":"Romberg","year":"2001","journal-title":"IEEE Trans. Image Process."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Donoho, D.L. (2001). Ridge Functions and Orthonormal Ridgelets, Academic Press, Inc.","DOI":"10.1006\/jath.2001.3568"},{"key":"ref_18","unstructured":"Emmanuel, J.C. (1999). Monoscale Ridgelets for the Representation of Images with Edges, Department of Statistics Stanford University. Technical Report."},{"key":"ref_19","unstructured":"Pennec, E.L., and Mallat, S. (2000, January 10\u201313). Image compression with geometrical wavelets. Proceedings of the IEEE International Conference on Image Processing, Vancouver, BC, Canada."},{"key":"ref_20","first-page":"597","article-title":"Relative radiometric correction methods for remote sensing images and their applicability analysis","volume":"18","author":"Duan","year":"2014","journal-title":"J. Remote Sens."},{"key":"ref_21","first-page":"111","article-title":"Application of Improved Grubbs\u2019 Criterion to Estimation of Signal Detection Threshold","volume":"31","author":"Shen","year":"1999","journal-title":"J. Harbin Inst. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/0034-4257(93)90061-2","article-title":"An operational method for estimating signal to noise ratios from data acquired with imaging spectrometers","volume":"43","author":"Gao","year":"1993","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/5\/759\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:04:20Z","timestamp":1760195060000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/5\/759"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,15]]},"references-count":22,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2018,5]]}},"alternative-id":["rs10050759"],"URL":"https:\/\/doi.org\/10.3390\/rs10050759","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,5,15]]}}}