Spectral image fusion using band reduction and contourlets

Spectral images have relatively low spatial resolution, compared to high-resolution single band panchromatic (PAN) images. Therefore, fusing a spectral image with a PAN image has been widely studied to produce a high-resolution spectral image. However, raw spectral images are too large to process and contain redundant information that is not utilized in the fusion process. In this study, we propose a novel fusion method that employs a spectral band reduction and contourlets. The band reduction begins with the best two band combination, and this two-band combination is subsequently augmented to three, four, and more until the desired number of bands is selected. The adopted band selection algorithm using the endmember extraction concept employs a sequential forward search strategy. Next, the image fusion is performed with two different spectral images based on the frequency components that are newly obtained by contourlet transform (CT). One spectral image that is used as a dataset is multispectral (MS) image and the other is hyperspectral (HS) image. Each original spectral image is pre-processed by spectrally integrating over the entire spectral range to obtain a PAN source image that is used in the fusion process. This way, we can eliminate the step of image co-registration since the obtained PAN image is already perfectly aligned to the spectral image. Next, we fuse the band-reduced spectral images with the PAN images using contourlet-based fusion framework. The resultant fusion image provides enhanced spatial resolution while preserving the spectral information. In order to analyze the band reduction performance, the original spectral images are fused with the same PAN images to serve as a reference image, which is then compared to the band-reduced spectral image fusion results using six different quality metrics.