IHS和小波变换结合多源遥感影像融合质量对小波分解层数的响应

随着遥感技术的快速发展以及遥感数据的广泛应用，影像的融合处理已成为多源遥感影像信息聚合、获取高质量空间影像的有效途径。基于SPOT全色和多光谱、TM多光谱遥感数据，运用IHS和小波变换相结合的融合方法，进行了不同来源影像融合、融合图像质量对小波分解层数的响应以及这种响应对研究区域面积的敏感性分析。结果表明，多源影像之间的IHS和小波变换相结合的融合方法明显地改善了影像的质量；融合图像质量与原始影像空间分辨率相关，如经1层小波变换融合，TM，SPOT融合图像熵值的增幅分别为2095%，019%。小波融合图像质量对小波分解的层数的敏感性较强，在小波分解层数为2，3或4时，都能获得高质量的融合图像；小波分解层数等于或大于5时融合图像质量下降，7是大幅下降的临界层数。融合图像质量对小波分解层数的响应特性对面积大小变化是敏感的，特别是小面积图像，为此，实际应用中需特别注意最佳分解层数问题。

Response of Fusion Images to Wavelet Decomposition Levels of Integration of Wavelet Transform and IHS with Multiple Sources Remotely Sensed Data

Due to rapid development of remote sensing technology and worldwide application of remotely sensed data, image fusion is an effective way to incorporate data from different remote sensors to create an improved image containing much more spectral and spatial details, and could be used to facilitate visually interpreting of remotely sensed imagery or subsequent mechanism analysis. It has been proved that better fusion images, which contain the spatial information of the panchromatic data and details of the multispectral, might be produced by the integration of wavelet transform with IHS. But the influence of some important parameters has been neglected in the practice of integration, such as levels of wavelet decomposition. Most commercially remote sensor data are composed of hundreds and millions pixels with a number of bands. Thus, the applicability of research conclusions has previously been suspicious. Therefore it is helpful to have a deep study to explore those questions.