一种基于小波包变换的SAR图像与TM图像融合方法

为了增强来自不同传感器的图像信息，改善图像的可分析和提取能力，近年来，常采用小波变换融合方法。但小波变换只对低频信息进行多分辨分析，并不考虑高频信息的多级分解。小波包变换不仅能对图像的低频部分，而且对小波变换没有细分的高频部分也能进一步地分解。因此，小波包分析能够为图像融合提供一种比小波多分辨分析更加精细的分析方法。在研究了小波包分析法后，提出了一种小波包图像融合方法。利用此融合算法对同一场景的不同传感器荻得的合成孔径雷达（SAR）图像和专题绘图仪（TM）图像进行融合，通过客观分析与目视评价，证明该融合方法的融合结果更好。

An Algorithm for Image Fusion Based on Wavelet Packet Transform

Existing WT (Wavelet Transform) fusion algorithm for enhancing the image information from multi-sensor and improving information analysis and feature extraction is not good enough because it does not consider the high frequency components of the images. In fact, the high frequency components of the images contain much detail information. To tackle this problem, we propose a new WPT (Wavelet Packet Transform) algorithm for fusing SAR (Synthetic Aperture Radar) image and TM (Thematic Mapper) image. Firstly, we employ HIS (hue, intensity, saturation) transform to obtain intensity component of TM multi-spectral image. Then we employ WPT transform to decompose the intensity component and SAR image into low frequency band and high frequency band in three levels. After that, two high frequency coefficients and low frequency coefficients of the image are combined by linear weighting strategies. Finally, the fused image is obtained with inverse WPT and inverse HIS. Visual result and statistical parameters show that the performance of our new algorithm is more efficient than that of HM (histogram matched)-based fusion algorithm and WT-based fusion algorithm. It not only greatly increases spatial detail information of the image, but also well preserves the spectral information of the image.