Copyright © 2013 Heesung Kwon et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Recent advances in multispectral and hyperspectral sensing technologies coupled with rapid growth in computing power have led to new opportunities...

In hyperspectral unmixing, the objective is to decompose an electromagnetic spectral dataset measured over M spectral bands and T pixels, into N constituent material spectra (or “endmembers”) with corresponding spatial abundances. In this paper, we propose a novel approach to hyperspectral unmixing (i.e., joint estimation of endmembers and abundances) based on loopy belief propagation. In parti...

This letter presents K-P-Means, a novel approach for hyperspectral endmember estimation. Spectral unmixing is formulated as a clustering problem, with the goal of K-P-Means to obtain a set of “purified” hyperspectral pixels to estimate endmembers. The K-P-Means algorithm alternates iteratively between two main steps (abundance estimation and endmember update) until convergence to yield final en...

As the volume of remotely sensed data grows significantly, content-based image retrieval (CBIR) becomes increasingly important, especially for cloud computing platforms that facilitate processing and storing big in a parallel distributed way. This paper proposes novel CBIR system hyperspectral (HSI) repository on under guide unmixed spectral information, i.e., endmembers their associated fracti...

Over the past decades, many methods have been proposed to solve linear or nonlinear mixing of spectra inside hyperspectral data. Due a relatively low spatial resolution imaging, each image pixel may contain from multiple materials. In turn, unmixing is finding these materials and their abundances. A few main approaches performing emerged, such as nonnegative matrix factorization (NMF), mixture ...

In this paper, we compare field programmable gate arrays (FPGAs) versus graphical processing units (GPUs) in the framework of (lossy) remotely sensed hyperspectral data compression by developing parallel implementations of a spectral unmixing-based compression strategy on both platforms. For the FPGA implementations, we resort to Xilinx hardware devices certified for on-board operation, while f...

The spatial resolution of hyperspectral image is often low due to the limitation of the imaging spectrometer. Fusing the original hyperspectral image with high-spatialresolution panchromatic image is an effective approach to enhance the resolution of hyperspectral image. However, it is hard to preserve the spectral information at the same time of enhancing the resolution by the traditional fusi...