Hyperspectral imagery super-resolution by sparse representation and spectral regularization

Abstract

For the instrument limitation and imperfect imaging optics, it is difficult to acquire high spatial resolution hyperspectral imagery. Low spatial resolution will result in a lot of mixed pixels and greatly degrade the detection and recognition performance, affect the related application in civil and military fields. As a powerful statistical image modeling technique, sparse representation can be utilized to analyze the hyperspectral image efficiently. Hyperspectral imagery is intrinsically sparse in spatial and spectral domains, and image super-resolution quality largely depends on whether the prior knowledge is utilized properly. In this article, we propose a novel hyperspectral imagery super-resolution method by utilizing the sparse representation and spectral mixing model. Based on the sparse representation model and hyperspectral image acquisition process model, small patches of hyperspectral observations from different wavelengths can be represented as weighted linear combinations of a small number of atoms in pre-trained dictionary. Then super-resolution is treated as a least squares problem with sparse constraints. To maintain the spectral consistency, we further introduce an adaptive regularization terms into the sparse representation framework by combining the linear spectrum mixing model. Extensive experiments validate that the proposed method achieves much better results.

Compact spatio-spectral algorithm for single image super-resolution in hyperspectral imaging

“…s 1 s 2 NM×1 and y ∈ ! NM×1 be the vector representations of a HR and its LR image version, respectively (Zhao, 2011), (Winter, 2002), (Tanaka, 2007 is the noise introduced by the sensing system. Examples of the structure of the matrices D and H are indicated in Figure 2.…”

“…Hence, researchers have investigated the use of resolution enhancement techniques by post processing as a better alter native to improve the image quality (Akgun, 2005), (Mianji, 2008), (Zhao, 2011). To improve the spatial resolution of hyperspectral images, traditional super-resolution (SR) techniques may be used.…”

Hyperspectral Image Super-Resolution via Nonlocal Low-Rank Tensor Approximation and Total Variation Regularization

“…Guo et al [15] used the unmixing information and total variation (TV) minimization to produce a higher resolution HSI. By modeling the sparse prior underlying HSIs, a sparse HSI super-resolution model was proposed in [16]. Zhang et al [17] proposed a maximum a posteriori based HSI super-resolution reconstruction algorithm, in which PCA is employed to reduce computational load and simultaneously remove noise.…”

Bayesian fusion of hyperspectral and multispectral images

“…Capitalizing on decades of experience in MS pansharpening, most of the HS pansharpening approaches merely adapt existing algorithms for PAN and MS fusion [7]. Other methods are specifically designed to the HS pansharpening problem such as [8] or [9,10] invoking super-resolution techniques. Conversely, the fusion of MS and HS images has been considered in fewer research works and is still a challenging problem because of the high dimensionality of the data to be processed.…”