基于小波统计特性的遥感图像像素与特征联合最优融合方法

肖刚; 敬忠良; Henry Leung; 王淑

doi:10.11834/jrs.20050455

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专辑

基于小波统计特性的遥感图像像素与特征联合最优融合方法
A United Optimal Fusion Method of Pixel and Feature for Remote Sensing Images Based on the Statistical Properties of Wavelet Decomposition
2005年第4期页码：381-386
纸质出版：2005
DOI： 10.11834/jrs.20050455
稿件说明：

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[1]肖刚,敬忠良,Henry Leung,王淑.基于小波统计特性的遥感图像像素与特征联合最优融合方法[J].遥感学报,2005(04):381-386. DOI： 10.11834/jrs.20050455.

XIAO Gang1, JING Zhong-liang1, Henry Leung2, et al. A United Optimal Fusion Method of Pixel and Feature for Remote Sensing Images Based on the Statistical Properties of Wavelet Decomposition[J]. Journal of Remote Sensing, 2005, (4): 381-386. DOI： 10.11834/jrs.20050455.

摘要

遥感影像的IHS融合方法由于匹配误差导致光谱畸变和退化

而小波变换在变换域具有良好的分频特性

小波系数的统计特性反映了遥感影像的边缘、线和区域等显著特征。提出了基于小波统计特性的遥感影像的像素和特征联合最优融合方法

在IHS空间

对强度分量I的高频部分利用多分辨率小波融合方法进行影像的高频细节特征融合

低频部分选取光谱信息和空间分辨率评价指标作为融合权系数求优指标

进行像素级最优融合

实验结果证明了该方法的有效性。

Abstract

Matching error causes spectral distortion and degradation in processing of remote sensing images fusion with IHS method. Wavelet decomposition has properties of frequency division in transform domain. And the statistical properties of wavelet coefficient reflect those significant features

such as edges

lines and regions. In this paper

a united optimal fusion method of pixel and feature is proposed based on the statistical properties of wavelet decomposition. In IHS space

the high frequency of intensity component I is fused at feature level with multi-resolution wavelet. And the low frequency of intensity component I is fused at pixel level with optimal weight coefficients. Spectral information and spatial resolution are two indexes of optimum weight coefficient. The test results with QuickBird and TM/SPOT data show the effectiveness of presented method.