A New Hyperspectral Pansharpening Method With Intrisic Image Decomposition

The component substitution (CS) and multiresolution analysis (MRA) based methods have been well adopted in hyperspectral pansharpening. The major contribution of this paper is a novel MRA and CS hybrid framework based on the intrinsic image decomposition. First, the weighted least squares (WLS) filter is performed on the sharpened panchromatic (P) image to extract the high-frequency component. Then, the intrinsic image decomposition (IID) is adopted to decompose the interpolated hyperspectral (H) image into the illumination and reflectance components. Finally, the detail map is generated by making a proper compromise between the high-frequency component of the P image and the illumination component of the H image. The detail map further refined by the information ratio of different bands of the H image is injected into each band of the interpolated H image. Experimental results indicate that the proposed method achieves a better fusion result than several state-of-the-art hyperspectral pansharpening methods.