基于子带小波特征的高分辨率遥感图像特征提取方法

Method for Feature Extraction of High Resolution Remote Sensing Image Based on the Characteristic of Image Wavelet Coefficients

基于统计理论模型对高分辨率遥感图像进行纹理特征提取。首先对高分辨率遥感图像进行小波变换,用广义高斯密度分布函数来描述变换后各子带小波系数的分布情况(小波系数直方图),建立遥感图像纹理特征的描述向量。通过对描述向量进行相似性比较,最终按照相似性递减的顺序输出检索结果。该方法简单、有效、计算速度快,适用于遥感图像的实时处理。特别是该方法所定义的图像相似性函数有可靠的理论依据,避免了大量主观因素的影响,符合基于内容图像检索的目的和要求。

A method for feature extraction of high resolution remote sensing image is presented which is based on the statistical model of the marginal distribution of wavelet coefficients. First,The wavelets are used to transform the high resolution remote sensing image into the frequency domain, then, used generalized Gaussian density （GGD） to model the marginal distribution of wavelet coefficients, and the result is used as the texture feature vector of the original image,finally, computed the Kullback-Leibler distance （KLD） between the texture feature vectors as similarity measurement（ SM）, and the output is ordered by the result of the SM. Experimental results show that this method is effective and efficient, and the image feature can be well represented by this texture feature vector. The advantage of this method is that the SM step can be computed entirely on the estimated model parameters, which has solid theoretic background, so that it can meet the requirements of the CBIR application.