Comparative analysis on landsat image enhancement using fractional and integral differential operators
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In this paper, Landsat image enhancement based on the fractional and integral differential methods is compared. Enhancement techniques used in remote sensing are based on the traditional integral order differential mask operators, such as Sobel, Prewitt and Laplacian operators. Other techniques involve the fractional calculus and general masks using Grünwald-Letnikov with eight directions. Notably, it is crucial to perform fractional filtering of I weight (intensity or brightness) in color space of (hue, saturation, intensity or brightness) according to the filtering rule. We study the quantitative analysis of enhancement performance via the gray-scale histogram and information entropy. Finally, we demonstrated that fractional differential operator is capable of enhanced performance superior to that of the integral differential operators. The optimal fractional order for image enhancement of Landsat Thematic Mapper is 1.15, 1.4, and 0.6 based on the (3 × 3), (5 × 5), and (7 × 7) masks, respectively.
KeywordsRemote sensing Image enhancement Fractional calculus Integral operators
Mathematics Subject Classification26A33 62H35 94A17 31A10
The work was supported by Department of Quanzhou Science and Technology (No. 2016N057), The authors also gratefully acknowledge the support of K.C. Wong Education and DAAD (No. 91551268). The authors also acknowledge the support by Fujian Provincial Key Laboratory of Data-Intensive Computing, Fujian University Laboratory of Intelligent Computing and Information Processing, and Fujian Provincial Big Data Research Institute of Intelligent Manufacturing.
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