Foreword to the Special Issue on Hyperspectral Remote Sensing and Imaging Spectroscopy

Heiden, Uta; Iwasaki, Akira; Müller, Andreas; Schlerf, Martin; Udelhoven, Thomas; Uto, Kuniaki; Yokoya, Naoto; Chanussot, Jocelyn

doi:10.1109/jstars.2016.2610199

Cited by 4 publications

(2 citation statements)

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“…The procedure of spectral unmixing works by decomposing the measured hyperspectral data into a collection of spectral signatures (spectral library) and a set of corresponding fractions (abundances) that represent the proportion of each spectral signature contained in the pixels [2,[5][6][7]. The spectral signatures that exist in the mixed pixels are called endmembers.…”

Section: Introductionmentioning

confidence: 99%

Joint Local Abundance Sparse Unmixing for Hyperspectral Images

Rizkinia

Okuda

2017

Remote Sensing

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Sparse unmixing is widely used for hyperspectral imagery to estimate the optimal fraction (abundance) of materials contained in mixed pixels (endmembers) of a hyperspectral scene, by considering the abundance sparsity. This abundance has a unique property, i.e., high spatial correlation in local regions. This is due to the fact that the endmembers existing in the region are highly correlated. This implies the low-rankness of the abundance in terms of the endmember. From this prior knowledge, it is expected that considering the low-rank local abundance to the sparse unmixing problem improves estimation performance. In this study, we propose an algorithm that exploits the low-rank local abundance by applying the nuclear norm to the abundance matrix for local regions of spatial and abundance domains. In our optimization problem, the local abundance regularizer is collaborated with the L 2,1 norm and the total variation for sparsity and spatial information, respectively. We conducted experiments for real and simulated hyperspectral data sets assuming with and without the presence of pure pixels. The experiments showed that our algorithm yields competitive results and performs better than the conventional algorithms.

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Section: Introductionmentioning

confidence: 99%

Joint Local Abundance Sparse Unmixing for Hyperspectral Images

Rizkinia

Okuda

2017

Remote Sensing

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“…Spectral imaging (SI) techniques capture spectral information at each spatial location of a scene to identify the composition and structure of a target [1]; for instance, in remote sensing to analyze land properties, in artwork to preserve paintings, and in biomedical imaging to detect anomalies and diseases [2]- [4].…”

Section: Introductionmentioning

confidence: 99%

Recovering spectral images from compressive measurements using designed coded apertures and Matrix Completion Theory

Gelvez

Rueda

Argüello

2017

I&U:EfD

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<p>Spectral imaging aims to capture and process a 3-dimensional spectral image with a large amount of spectral information for each spatial location. Compressive spectral imaging techniques (CSI) increases the sensing speed and reduces the amount of collected data compared to traditional spectral imaging methods. The coded aperture snapshot spectral imager (CASSI) is an optical architecture to sense a spectral image in a single 2D coded projection by applying CSI. Typically, the 3D scene is recovered by solving an L1-based optimization problem that assumes the scene is sparse in some known orthonormal basis. In contrast, the matrix completion technique (MC) allows to recover the scene without such prior knowledge. The MC reconstruction algorithms rely on a low-rank structure of the scene. Moreover, the CASSI system uses coded aperture patterns that determine the quality of the estimated scene. Therefore, this paper proposes the design of an optimal coded aperture set for the MC methodology. The designed set is attained by maximizing the distance between the translucent elements in the coded aperture. Visualization of the recovered spectral signals and simulations over different databases show average improvement when the designed coded set is used between 1-3 dBs compared to the complementary coded aperture set, and between 3-9 dBs compared to the conventional random coded aperture set.</p>

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