Papers by Kevin Aguilar Arias
IEEE Geoscience and Remote Sensing Letters, 2014
Unlike multispectral (MSI) and panchromatic (PAN) images, generally the spatial resolution of hyp... more 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 an 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 wellposed inverse problem. As a regularizer, we employ sparse coding (SC), 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 SC methods, the proposed method is shown to significantly increase the spatial resolution while perserving the spectral content of the HSI. Index Terms-Fusion, hyperspectral images (HSI), multispectral images (MSI), sparse coding (SC), spectral unmixing. I. INTRODUCTION R EMOTE sensing images have been widely used in different practical applications such as earth surface monitoring, agriculture, forest monitoring, environmental studies, and military applications [1]. The main types of remote sensing images are panchromatic (PAN), multispectral (MSI), and hyperspectral images (HSI). PAN images have a high spatial resolution and spatial structures are well defined, but they are limited to one gray-scale image band. MSI have lower spatial resolution than PAN images and contain a limited number of spectral bands. HSI usually have lower spatial resolution than MSI and PAN images but have a high spectral resolution [2],
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Papers by Kevin Aguilar Arias