Pansharpening via sparse regression

Abstract

Abstract. Pansharpening is an effective way to enhance the spatial resolution of a multispectral (MS) image by fusing it with a provided panchromatic image. Instead of restricting the coding coefficients of low-resolution (LR) and high-resolution (HR) images to be equal, we propose a pansharpening approach via sparse regression in which the relationship between sparse coefficients of HR and LR MS images is modeled by ridge regression and elastic-net regression simultaneously learning the corresponding dictionaries. The compact dictionaries are learned based on the sampled patch pairs from the high-and low-resolution images, which can greatly characterize the structural information of the LR MS and HR MS images. Later, taking the complex relationship between the coding coefficients of LR MS and HR MS images into account, the ridge regression is used to characterize the relationship of intrapatches. The elastic-net regression is employed to describe the relationship of interpatches. Thus, the HR MS image can be almost identically reconstructed by multiplying the HR dictionary and the calculated sparse coefficient vector with the learned regression relationship. The simulated and real experimental results illustrate that the proposed method outperforms several well-known methods, both quantitatively and perceptually.

Hyperspectral Image Super-Resolution via Adaptive Dictionary Learning and Double ℓ 1 Constraint

“…Traditionally, spatial-spectral image fusion methods fuse an LR HSI with an HR panchromatic image (single band), such as pansharpening [9]. As we know, the famous pansharpening methods include intensity-hue-saturation (IHS) [10,11], high-frequency information injection [12,13], and model-based methods [14][15][16]. Owing to the limited spectral resolution of the panchromatic image, these methods often produce some spectral distortions.…”

“…However, it is not effective to enhance spatial resolution by improving the imaging quality of the hyperspectral sensors, and a breakthrough in hardware will be difficult and costly. Alternatively, HSI super-resolution (SR) has been proposed to generate a high spatial resolution (HR) HSI by fusing a high spectral resolution image, such as HSI, with an image, such as panchromatic image [9][10][11][12][13][14][15][16] or a multispectral image (MSI) [17][18][19][20][21][22][23][24][25][26][27][28][29][30].…”