Fusion of Multispectral and Panchromatic Images via Local Geometrical Similarity

Abstract

A pansharpening method based on local geometrical similarity is proposed in this paper. According to the observation model, the relationships among low spatial resolution multispectral (LRMS), panchromatic (Pan) and high spatial resolution multispectral (HRMS) images are formulated. In this paper, in order to reduce the color distortion and enhance the spatial information of fused images, we propose a Pan-Sharpening method via Local Geometrical Similarity (PLGS). First, the structure similarity prior within a local region in the Pan image is employed to regularize the solution space to obtain a more accurate solution. Then, the prior is embedded into the LRMS image to enhance the spatial resolution. In order to capture better geometrical structure information, such as orientation information and geometric textures, the steerable kernel is used to calculate the similarity coefficients in a local window. Some experiments are considered on different datasets and the results show that the proposed method can improve the visual effect and the quantitative values.

Accelerated Proximal Algorithm for Finding the Dantzig Selector and Source Separation Using Dictionary Learning

“…Fourier transform, wavelet transform, sine and cosine transforms etc. In CS, original signal is recovered from far fewer samples as compared to the conventional Shannon theory [1][2][3][4][5]. Most of the practical signals are sparse in one or other domain, for example the sinusoid shaped signals are mostly sparse in the Fourier domain unlike images which have sparse representation in cosine domain.…”