摘要
IHS和小波变换结合的融合方法已成为多源遥感影像信息聚合的有效途径,但小波基与小波分解层数等参数对影像融合质量影响的很多细节需进一步研究。该文以SPOT遥感影像为数据,在Matlab软件环境下进行小波簇、不同序号小波基及分解层数的图像融合,用信息熵、平均梯度和标准偏差3指标对融合图像的质量进行评价。结论如下:第1层小波分解的融合图像质量差异不大;其余小波分解层数下融合影像的质量因小波基而异,如coif5s、ym5、dmey对小波分解层数不敏感,db1、bior3.1则具有分段响应的敏感特征,其中,rbio3.1在小波分解层数为4时,融合图像失真;不同簇的小波基对融合图像质量的影响也各异。结果表明,小波基等参数的选取直接影响到遥感影像融合的效果。
The integration of wavelet transform with IHS has apparently become popular to incorporate multi-sources remotely sensed data to create a new image,in that better fusion results might be produced by the method. But related factors are poorly considered, which would directly affect the fusion results, such as wavelet basis, decomposition levels. In Matlab,SPOT images, including panchromatic band and three multispectral bands, were used as analytical data here. Fusion images were created by highlighting those factors., different cluster of wavelets, wavelet basis with different serial number, and decomposition levels. Three indices for image performance, including entropy （joint entropy）, average gradient and deviation, were calculated. All fusion images had similar performance with only one wavelet decomposition level. But fusion images had different performance with increasing levels and different wavelet basis. Three wavelet basis,including coifS, symS, dmey, did not show different performance with changing levels. Two wavelet basis, db1 and bior3.1, had unique response characteristic within different ranges of level,such as indices value were stable while levels between 1 and 4, and then monotonically decreasing. Taking rbio3. 1 as wavelet basis, the distortion image was created when the wavelet decomposition levels was equal to 4. With the increasing levels, fusion images were created and characterized by dramatic distortion in image spectral. Besides, different cluster of wavelets in Matlab has different fusion image in performance. The paper could better support the practical application of remote sensed data in defining method to improve image performance by fusion multi-sources data and make the operation simpler and faster.
出处
《地理与地理信息科学》
CSSCI
CSCD
北大核心
2010年第2期6-10,F0002,共6页
Geography and Geo-Information Science
基金
国家自然科学基金重点项目(40635029)
中国博士后科学基金项目(20080440511)
中国博士后科学基金特别基金项目(200902132)
广州市属高校科技计划项目(08C027)
关键词
图像融合
小波变换
小波基
小波分解层数
fusion of image
wavelet transform
wavelet basis
wavelet decomposition level
