摘要
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。
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. Different types of remote sensed data were used in the study. SPOT 2. 5 × 2. 5 m panchromatic data with SPOT 10 × 10 m muhispectral data, and TM 15 × 15 m panchromatic band with 30 × 30 m muhispectral data. The integration of wavelet transform with IHS was adopted and several aspects were considered, such as the response of fusion image performance to the levels of wavelet decomposition, and also the response of sensitivity to the study area size. The results showed that fusion images with different source data was a very useful technique to integrate wavelet transform with IHS to acquire better performance of the images with spectral and spatial details. The performance of fusion images was closely related to the difference of raw resolution. For instance, the increment rate of joint entropy of fusion images with TM and SPOT data were 20. 95% and 0. 19% respectively under 1 level of wavelet decomposition. But the deviation factor can＇ t be deducted like above. Generally, 2, 3 and 4 levels of wavelet decomposition are the appropriate levels to meet application request, and oppositely higher levels could reduce quality of performance of fusion image. The results of this paper revealed greatly poor images were created under 7 or more than levels of wavelet decomposition for fusion. Here 7 levels could be considered as threshold level. Another result showed that this characteristic to be related to levels of wavelet decomposition was also sensitive to study area size. Therefore, when study area was selected in very small region, the optimal level of image fusion by wavelet must be considered as a very important factor for creating higher qualitative fusion images.
出处
《中国图象图形学报》
CSCD
北大核心
2010年第8期1269-1277,共9页
Journal of Image and Graphics
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(200902132
20080440511)
广州市属高校科技计划项目(08C027)
关键词
小波分解层数
图像融合
小波变换
IHS遥感数据
wavelet decomposition level, fusion, wavelet transform, IHS, Remotely sensed data
