Abstract
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.
Highlights
Hyperspectral pansharpening aims to combine the preponderance and complementary information of the hyperspectral (HS) and panchromatic (P) images for image analysis and various applications [1]
Six representative hyperspectral pansharpening methods are utilized for comparison, i.e., principal component analysis (PCA) [18], Guided filter PCA (GFPCA) [41], HySure [8], coupled nonnegative matrix factorization (CNMF) [10], modulation transfer function (MTF)-GLP with High Pass Modulation (MGH) [26] and Sparse Representation [7]
A novel hyperspectral pansharpening method based on intrinsic image decomposition (IID) and weighted least squares (WLS) filter has been presented in this paper
Summary
Hyperspectral pansharpening aims to combine the preponderance and complementary information of the hyperspectral (HS) and panchromatic (P) images for image analysis and various applications [1]. The fused HS image is obtained by injecting the extracted spatial details into each band of the interpolated HS image. To overcome the problems of the CS and MRA methods, the CS-MRA hybrid frameworks were proposed [20,28,29] These methods focus on fusing the P image and the spatial component of the HS image by multiscale transforms. (b) Unlike the traditional CS and MRA methods where the spatial details are just extracted from the P image, the detail map in the proposed method depends on both the HS image and the P image. The WLS filter is adopted to extract the high-frequency component of the P image in the proposed method.
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