Automatic SAR image enhancement based on curvelet transform and genetic algorithm

Authors:
Jie Hu;Ying Li;Yu Jia
Affiliations:
School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China;School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China;School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China
Venue:
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2011

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Abstract

This paper presents an automatic enhancement method for SAR images based on the mirror-extended curvelet transform and genetic algorithm. Firstly, an improved gain function which integrates the speckle reduction with the feature enhancement is proposed to nonlinearly shrink and stretch the curvelet coefficients, and then the genetic algorithm (GA) is used to automatically adjust the parameters of the gain function. We propose an objective criterion for enhancement, and attempt to find the (near) optimal image according to the respective criterion. We employ the GA as a global search strategy for the best enhancement which has a satisfactory compromise between sharpening and smoothing. The experimental results show that the proposed method can efficiently enhance the edge features and contrast of SAR images and reduce the speckle noises, and outperforms the wavelet- and curvelet-based non-automatic enhancement methods.