Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.Comment: This work has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensin

Dagstuhl Reports : Volume 1, Issue 2, February 2011

Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-Hübner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro Pezzé, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn

Networked Computing in Wireless Sensor Networks for Structural Health Monitoring

This paper studies the problem of distributed computation over a network of wireless sensors. While this problem applies to many emerging applications, to keep our discussion concrete we will focus on sensor networks used for structural health monitoring. Within this context, the heaviest computation is to determine the singular value decomposition (SVD) to extract mode shapes (eigenvectors) of a structure. Compared to collecting raw vibration data and performing SVD at a central location, computing SVD within the network can result in significantly lower energy consumption and delay. Using recent results on decomposing SVD, a well-known centralized operation, into components, we seek to determine a near-optimal communication structure that enables the distribution of this computation and the reassembly of the final results, with the objective of minimizing energy consumption subject to a computational delay constraint. We show that this reduces to a generalized clustering problem; a cluster forms a unit on which a component of the overall computation is performed. We establish that this problem is NP-hard. By relaxing the delay constraint, we derive a lower bound to this problem. We then propose an integer linear program (ILP) to solve the constrained problem exactly as well as an approximate algorithm with a proven approximation ratio. We further present a distributed version of the approximate algorithm. We present both simulation and experimentation results to demonstrate the effectiveness of these algorithms

Exploring the NRO Opportunity for a Hubble-sized Wide-field Near-IR Space Telescope -- NEW WFIRST

We discuss scientific, technical and programmatic issues related to the use of an NRO 2.4m telescope for the WFIRST initiative of the 2010 Decadal Survey. We show that this implementation of WFIRST, which we call "NEW WFIRST," would achieve the goals of the NWNH Decadal Survey for the WFIRST core programs of Dark Energy and Microlensing Planet Finding, with the crucial benefit of deeper and/or wider near-IR surveys for GO science and a potentially Hubble-like Guest Observer program. NEW WFIRST could also include a coronagraphic imager for direct detection of dust disks and planets around neighboring stars, a high-priority science and technology precursor for future ambitious programs to image Earth-like planets around neighboring stars.Comment: 76 pages, 26 figures -- associated with the Princeton "New Telescope Meeting

Wide-Field InfraRed Survey Telescope (WFIRST) Final Report

In December 2010, NASA created a Science Definition Team (SDT) for WFIRST, the Wide Field Infra-Red Survey Telescope, recommended by the Astro 2010 Decadal Survey as the highest priority for a large space mission. The SDT was chartered to work with the WFIRST Project Office at GSFC and the Program Office at JPL to produce a Design Reference Mission (DRM) for WFIRST. Part of the original charge was to produce an interim design reference mission by mid-2011. That document was delivered to NASA and widely circulated within the astronomical community. In late 2011 the Astrophysics Division augmented its original charge, asking for two design reference missions. The first of these, DRM1, was to be a finalized version of the interim DRM, reducing overall mission costs where possible. The second of these, DRM2, was to identify and eliminate capabilities that overlapped with those of NASA's James Webb Space Telescope (henceforth JWST), ESA's Euclid mission, and the NSF's ground-based Large Synoptic Survey Telescope (henceforth LSST), and again to reduce overall mission cost, while staying faithful to NWNH. This report presents both DRM1 and DRM2.Comment: 102 pages, 57 figures, 17 table