A practical pan-sharpening method with wavelet transform and sparse representation

Abstract

Pan-sharpening is an important remote sensing image pre-processing technique, which aims at obtaining a high-resolution multispectral (HRM) image by integrating the spectral information of a low-resolution multispectral (LRM) image and the spatial details of a high-resolution panchromatic (HRP) image. This paper proposes a new pan-sharpening method with sparse representation (SR) under the framework of wavelet transform. First, the wavelet transform is applied to the HRP image and the intensity component of LRM image. Then, the low-frequency components are fused based on SR to extract the spatial details in the HRP image as much as possible, and the dictionary is simply learned from high-quality nature images. Moreover, a novel strategy is also proposed to preserve the spectral information in the LRM image. On the other hand, the “numerous-but-sparse” high-frequency components are merged based on the local wavelet energy, which makes the algorithm more efficient than traditional SR-based methods. Finally, the fused result is obtained by performing inverse wavelet transform and inverse IHS transform. Experiments on WorldView-2 images demonstrate that the proposed method gives more spatial details and less spectral distortion compared with some conventional methods in terms of both visual quality and objective measurements.