基于高斯混合模型的遥感数字图像增强

遥感图像受云雾影响对比度低，为了提高图像质量保留图像细节，提出了一种基于高斯混合模型的遥感图像增强算法。应用1×3滤波器平滑原图像的直方图再用期望最大化(EM)算法对直方图进行拟合，获取高斯混合模型的聚类最优参数，并根据聚类的有效交点将直方图分区。由高斯参数确定输出图像所属聚类的映射关系，得到最终的增强图像。实验结果表明，该方法能自适应确定最佳聚类个数，提高直方图拟合的运算速度，平均处理时间提高到0.37 s，在相关信息熵和纹理信息等的客观评价中，增强结果明显优于传统方法。有效地提高了遥感图像的对比度，同时保持了图像的细节信息。

Abstract

The remote sensing image is susceptible to the clouds and fog. In order to improve the output quality of low contrast image and maintain the details, an image enhancement algorithm based on Gaussian mixture modeling (GMM) is proposed. The histogram of original image is smoothed with a 1×3 filter. The best parameters of GMM is got by fitting the histogram with the expectation maximization (EM) algorithm, and the histogram is separated into sub-histograms based on the optimal intersections. The mapping of output image is got according to the Gaussian parameters, and the final enhanced image is obtained. Results of experiments show that the algorithm can determine the optimal number of clusters adaptively and improve the speed of the histogram fitting which costs 0.37 s averagely. Comparing with traditional methods, the enhancement result is superior in terms of objective evaluations of related information entropy and texture information. It can improve the contrast of the remote sensing image while maintaining the details effectively.

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