Hyperspectral Pansharpening Based on Intrinsic Image Decomposition and Weighted Least Squares Filter

Component substitution (CS) and multiresolution analysis (MRA) based methods have been adopted in hyperspectral pansharpening. The major contribution of this paper is a novel CS-MRA hybrid framework based on intrinsic image decomposition and weighted least squares filter. First, the panchromatic (P) image is sharpened by the Gaussian-Laplacian enhancement algorithm to enhance the spatial details, and the weighted least squares (WLS) filter is performed on the enhanced P image to extract the high-frequency information of the P image. Then, the MTF-based deblurring method is applied to the interpolated hyperspectral (HS) image, and the intrinsic image decomposition (IID) is adopted to decompose the deblurred interpolated HS image into the illumination and reflectance components. Finally, the detail map is generated by making a proper compromise between the high-frequency information of the P image and the spatial information preserved in the illumination component of the HS image. The detail map is further refined by the information ratio of different bands of the HS image and injected into the deblurred interpolated HS image. Experimental results indicate that the proposed method achieves better fusion results than several state-of-the-art hyperspectral pansharpening methods. This demonstrates that a combination of an IID technique and a WLS filter is an effective way for hyperspectral pansharpening.