Space object characterization from spectral nonimaging data

Luu, Kim; Snodgrass, Joshua; Matson, Charles L.; Giffin, S. Maile; Hamada, Kris; Lambert, John V.

doi:10.1364/fio.2003.tuk2

Cited by 10 publications

(16 citation statements)

References 1 publication

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“…The noise is chosen to be 1% Gaussian, that is N i F = 0.01 * Y i F / R i F where the entries in R i are randomly chosen from a Gaussian distribution N(0, 1). Representative simulated spectral traces from each sub-data set Y i are shown in Figure 4 and are consistent with simulated and real data employed in related work (Luu et al, 2003).…”

Section: Simulation Setupsupporting

confidence: 78%

“…Laboratory spectral signatures for aluminum, mylar, solar cell, and white paint. For details see (Luu et al, 2003).…”

Section: Spectral Unmixing For Non-resolved Space Object Characterizamentioning

confidence: 99%

“…Hence, like other researchers (Luu et al, 2003), we employed laboratory-obtained spectral signatures for the creation of simulated data using the linear mixing model of equation (11).…”

Section: Simulation Setupmentioning

confidence: 99%

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Algorithms and applications for approximate nonnegative matrix factorization

Berry

Browne

Langville

et al. 2007

Computational Statistics & Data Analysis

1,290

907

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In this paper we discuss the development and use of low-rank approximate nonnegative matrix factorization (NMF) algorithms for feature extraction and identification in the fields of text mining and spectral data analysis. The evolution and convergence properties of hybrid methods based on both sparsity and smoothness constraints for the resulting nonnegative matrix factors are discussed. The interpretability of NMF outputs in specific contexts are provided along with opportunities for future work in the modification of NMF algorithms for large-scale and time-varying datasets.

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Section: Simulation Setupsupporting

confidence: 78%

“…Laboratory spectral signatures for aluminum, mylar, solar cell, and white paint. For details see (Luu et al, 2003).…”

Section: Spectral Unmixing For Non-resolved Space Object Characterizamentioning

confidence: 99%

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Algorithms and applications for approximate nonnegative matrix factorization

Berry

Browne

Langville

et al. 2007

Computational Statistics & Data Analysis

1,290

907

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“…The four materials were 1 S , a tan/yellow target;2 S , a beige/yellow target; 3 S , a very white coating on aluminum; and 4 S , the red coating from the original experiments. Each supercluster was composed of 114 spectra.…”

mentioning

confidence: 99%

Improving the hyperspectral linear unmixing problem with unsupervised cluster and covariance estimates

Brevdo

Luu

2006

SPIE Proceedings

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The hyperspectral subpixel detection and classification problem has been intensely studied in the downward-looking case, typically satellite imagery of agricultural and urban areas. In contrast, the hyperspectral imaging case when "looking up" at small or distant satellites creates new and unforeseen problems. Usually one pixel or one fraction of a pixel contains the imaging target, and spectra tend to be time-series data of a single object collected over some time period under possibly varying weather conditions; there is little spatial information available. Often, the number of collected traces is less than the number of wavelength bins, and a materials database with imperfect representative spectra must be used in the subpixel classification and unmixing process. A procedure is formulated for generating a "good" set of classes from experimentally collected spectra by assuming a Gaussian distribution in the angle-space of the spectra. Specifically, Kernel K-means, a suboptimal ML-estimator, is used to generate a set of classes. Covariance information from the resulting classes and weighted least squares methods are then applied to solve the linear unmixing problem. We show with cross-validation that Kernel K-means separation of laboratory material classes into "smaller" virtual classes before unmixing improves the performance of weighted least squares methods.

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“…The goal of the Tetracorder project was to identify surface materials on the planet to aid in mapping resources using hyperspectral imaging. In this case the materials which are measured are actually separable pixel by pixel, so that identification can be Another project in the early 2000's attempted spectral unmixing on spacecraft assuming a linear combination of spectra [15] [16], [17]. Their method utlized non-negative matrix factorization, and was similar to the methods developed in this thesis, though less effective at determing material composition.…”

Section: -Previous Workmentioning

confidence: 99%

Identification of Orbital Objects by Spectral Analysis and Observation of Space Environment Effects

Rapp¹

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This report presents an investigation and development of the methods for orbital object identification. Two goals were accomplished in this master's thesis; the development of a method of inverting material proportions from an object's combined spectrum, and the investigation of methods and initialization of measurement of space environment effects on spectral features of common spacecraft materials. A constrained least squares approach was chosen for inverting spectral proportions from the combined spectra. The final results fall within 1 -15% of the original spectrum, depending on the quality and noise levels of the original spectrum. Additionally, the effects of outgassing and atomic oxygen erosion were measured using the vacuum chamber facilities at California Polytechnic State University and are to be used as a basis for future identification of orbital debris. To have a fully functional model for accurately identifying space objects, both parts are needed: a set of space environment effect measurements as a basis for the identification model (for use on objects exposed to the space environment), and the identification model to mathematically determine the best fit set of materials. | v ACKNOWLEDGEMENTS

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Space object characterization from spectral nonimaging data

Cited by 10 publications

References 1 publication

Algorithms and applications for approximate nonnegative matrix factorization

Algorithms and applications for approximate nonnegative matrix factorization

Improving the hyperspectral linear unmixing problem with unsupervised cluster and covariance estimates

Identification of Orbital Objects by Spectral Analysis and Observation of Space Environment Effects

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