Fusion of Hyperspectral and Multispectral Images Using Spectral Unmixing and Sparse Coding

Abstract

Unlike multispectral (MSI) and panchromatic (PAN) images, generally the spatial resolution of hyperspectral images (HSI) is limited, due to sensor limitations. In many applications, HSI with a high spectral as well as spatial resolution are required. In this paper, a new method for spatial resolution enhancement of a HSI using spectral unmixing and sparse coding (SUSC) is introduced. The proposed method fuses high spectral resolution features from the HSI with high spatial resolution features from a MSI of the same scene. Endmembers are extracted from the HSI by spectral unmixing and the exact location of the endmembers is obtained from the MSI. This fusion process by using spectral unmixing is formulated as an ill-posed inverse problem which requires a regularization term in order to convert it into a well-posed inverse problem. As a regularizer, we employ sparse coding, for which a dictionary is constructed using high spatial resolution MSI or PAN images from unrelated scenes. The proposed algorithm is applied to real Hyperion and ROSIS datasets. Compared with other state-of-the-art algorithms based on pansharpening, spectral unmixing and sparse coding methods, the proposed method is shown to significantly increase the spatial resolution while perserving the spectral content of the HSI.

Spatial Resolution Enhancement of Hyperspectral Images Using Spectral Unmixing and Bayesian Sparse Representation

“…In the experimental section, the proposed method has been compared with the spectral unmixing based fusion method CNMF [5], the sparse coding based fusion methods SC [13] and Bayesian sparse coding [15], and an earlier developed method (SUSC) that combines spectral unmixing and sparse coding [17].…”

“…We see two reasons for this. First, the SC-based methods do not take into account the Gaussian noise model in the fusion process (see Equation (6)); second, they use two-dimensional dictionaries, i.e., the dictionaries are the same for all bands [17].…”

“…We denoised them by using the Next ESA SAR Toolbox (NEST) (https://earth.esa.int/web/nest/home). The proposed method is compared to the following popular fusion algorithms: the spectral unmixing based fusion method CNMF [5], the sparse representation based fusion methods SC [13] and Bayesian sparse coding [15], our earlier combined method SUSC [17], and Hysure [7]. In CNMF, the maximum numbers of iterations in the inner and outer loops are selected as 100 and 1, respectively.…”

“…Recently, we introduced a fusion method based on the combination of spectral unmixing and sparse coding (SUSC) [17]. This method showed better performance than the spectral unmixing [18] and sparse coding [13] methods.…”

“…By using a combination of SU and BS, the proposed method (SUBS) simultaneously increases the spatial resolution and decreases the spectral distortions.The proposed method is applied to real datasets and compared with the spectral unmixing based method CNMF [5], the sparse representation methods SC [13] and BS [15], and the combined method SUSC [17]. The remaining of the paper is organized as follows.…”