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Remote Sens. 2015, 7(9), 11848-11862; doi:10.3390/rs70911848

An Uneven Illumination Correction Algorithm for Optical Remote Sensing Images Covered with Thin Clouds

Shenzhen Key Laboratory of Spatial Smart Sensing and Services & Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation, Shenzhen University, Shenzhen 518060, China
College of Information Engineering, Shenzhen University, Shenzhen 518060, China
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
Author to whom correspondence should be addressed.
Academic Editors: Gonzalo Pajares Martinsanz, Richard Müller and Prasad S. Thenkabail
Received: 4 June 2015 / Revised: 6 September 2015 / Accepted: 8 September 2015 / Published: 16 September 2015
View Full-Text   |   Download PDF [1079 KB, uploaded 16 September 2015]   |  


The uneven illumination phenomenon caused by thin clouds will reduce the quality of remote sensing images, and bring adverse effects to the image interpretation. To remove the effect of thin clouds on images, an uneven illumination correction can be applied. In this paper, an effective uneven illumination correction algorithm is proposed to remove the effect of thin clouds and to restore the ground information of the optical remote sensing image. The imaging model of remote sensing images covered by thin clouds is analyzed. Due to the transmission attenuation, reflection, and scattering, the thin cloud cover usually increases region brightness and reduces saturation and contrast of the image. As a result, a wavelet domain enhancement is performed for the image in Hue-Saturation-Value (HSV) color space. We use images with thin clouds in Wuhan area captured by QuickBird and ZiYuan-3 (ZY-3) satellites for experiments. Three traditional uneven illumination correction algorithms, i.e., multi-scale Retinex (MSR) algorithm, homomorphic filtering (HF)-based algorithm, and wavelet transform-based MASK (WT-MASK) algorithm are performed for comparison. Five indicators, i.e., mean value, standard deviation, information entropy, average gradient, and hue deviation index (HDI) are used to analyze the effect of the algorithms. The experimental results show that the proposed algorithm can effectively eliminate the influences of thin clouds and restore the real color of ground objects under thin clouds. View Full-Text
Keywords: HSV transform; wavelet analysis; remote sensing images; uneven illumination; thin clouds HSV transform; wavelet analysis; remote sensing images; uneven illumination; thin clouds

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Shen, X.; Li, Q.; Tian, Y.; Shen, L. An Uneven Illumination Correction Algorithm for Optical Remote Sensing Images Covered with Thin Clouds. Remote Sens. 2015, 7, 11848-11862.

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