Remote sensing image fusion via wavelet transform and sparse representation

In this paper, we propose a remote sensing image fusion method which combines the wavelet transform and sparse representation to obtain fusion images with high spectral resolution and high spatial resolution. Firstly, intensity-hue-saturation (IHS) transform is applied to Multi-Spectral (MS) images. Then, wavelet transform is used to the intensity component of MS images and the Panchromatic (Pan) image to construct the multi-scale representation respectively. With the multi-scale representation, different fusion strategies are taken on the low-frequency and the high-frequency sub-images. Sparse representation with training dictionary is introduced into the low-frequency sub-image fusion. The fusion rule for the sparse representation coefficients of the low-frequency sub-images is defined by the spatial frequency maximum. For high-frequency sub-images with prolific detail information, the fusion rule is established by the images information fusion measurement indicator. Finally, the fused results are obtained through inverse wavelet transform and inverse IHS transform. The wavelet transform has the ability to extract the spectral information and the global spatial details from the original pairwise images, while sparse representation can extract the local structures of images effectively. Therefore, our proposed fusion method can well preserve the spectral information and the spatial detail information of the original images. The experimental results on the remote sensing images have demonstrated that our proposed method could well maintain the spectral characteristics of fusion images with a high spatial resolution.