&lt;title&gt;N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data&lt;/title&gt;

Winter, Michael E.

doi:10.1117/12.366289

Cited by 1,305 publications

(886 citation statements)

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“…Namely, the source signals are only required to be non-overlapping at some locations of acquisition variable. This sparseness condition was first known in the 1990s [1,22] in the study of blind hyper-spectral unmixing of remote sensing, where the source condition is called pixel purity assumption (PPA) [2]. In 2005, Naanaa and Nuzillard [14] used this assumption to separate the signals in nuclear magnetic resonance spectroscopy.…”

Section: Convex Blind Source Separationmentioning

confidence: 99%

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A sparse semi-blind source identification method and its application to Raman spectroscopy for explosives detection

Sun

Xin

2014

Signal Processing

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Rapid and reliable detection and identification of unknown chemical substances is critical to homeland security. It is challenging to identify chemical components from a wide range of explosives. There are two key steps involved. One is a nondestructive and informative spectroscopic technique for data acquisition. The other is an associated library of reference features along with a computational method for feature matching and meaningful detection within or beyond the library.Recently several experimental techniques based on Raman scattering have been developed to perform standoff detection and identification of explosives, and they prove to be successful under certain idealized conditions. However data analysis is limited to standard least squares method assuming the complete knowledge of the chemical components. In this paper, we develop a new iterative method to identify unknown substances from mixture samples of Raman spectroscopy. In the first step, a constrained least squares method decomposes the data into a sum of linear combination of the known components and a non-negative residual. In the second step, a sparse and convex blind source separation method extracts components geometrically from the residuals. Verification based on the library templates or expert knowledge helps to confirm these components. If necessary, the confirmed meaningful components are fed back into step one to refine the residual and then step two extracts possibly more hidden components. The two steps may be iterated until no more components can be identified. We illustrate the proposed method in processing a set of the so called swept wavelength optical resonant Raman spectroscopy experimental data by a satisfactory blind extraction of a priori unknown chemical explosives from mixture samples.

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Section: Convex Blind Source Separationmentioning

confidence: 99%

“…In the context of hyper-spectral unmixing, the resulting geometric (cone) method is the so called N-findr [22], and is now a benchmark in hyper-spectral unmixing. Next we shall review the essentials of the partial spareness condition and the geometric cone method.…”

Section: Convex Blind Source Separationmentioning

confidence: 99%

A sparse semi-blind source identification method and its application to Raman spectroscopy for explosives detection

Sun

Xin

2014

Signal Processing

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“…This presents algorithmic challenges for solving the unmixing problem. Several algorithms have been proposed in the context of hyperspectral imaging to solve similar problems [8,9]. Most of these algorithms perform unmixing in a two step procedure where M is estimated first using an endmember extraction algorithm (EEA) followed by a constrained linear least squares step to solve for A.…”

Section: Mathematical Modelmentioning

confidence: 99%

Unsupervised Bayesian analysis of gene expression patterns

Bazot

Dobigeon

Tourneret

et al. 2010

2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers

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In this paper we introduce a new method for analyzing expression patterns from high throughput and complex data such as gene expression microarrays. These microarrays are collected under different conditions such as time, phenotype and treatment. The proposed method uses a Bayesian matrix decomposition, called Bayesian linear unmixing (BLU), to extract a set of characteristic gene signatures, or factors, and a set of coefficients, factor scores, that specify the relative contribution of each signature to a specific sample. BLU is related to Bayesian factor analysis but differs in an important respect: BLU constrains the factor loadings to be non-negative and the factor scores to be probability distributions over the factors. Thus BLU reduces the multiplexing of genes into different factors and can enhance interpretability of the factor loadings and factor scores. The unsupervised version of BLU presented in this paper also provides estimates of the number of factors. We illustrate the application of BLU to bioinformatics by analyzing gene expression microarray datasets.

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“…Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the pixel purity index (PPI) [15] and the N-FINDR [18] still find the maximum volume simplex that contains the data cloud. They assume the presence of at least one pure pixel of each endmember in the data.…”

Section: Related Workmentioning

confidence: 99%

Two Linear Unmixing Algorithms to Recognize Targets Using Supervised Classification and Orthogonal Rotation in Airborne Hyperspectral Images

Averbuch

Zheludev

2012

Remote Sensing

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The goal of the paper is to detect pixels that contain targets of known spectra. The target can be present in a sub-or above pixel. Pixels without targets are classified as background pixels. Each pixel is treated via the content of its neighborhood. A pixel whose spectrum is different from its neighborhood is classified as a "suspicious point". In each suspicious point there is a mix of target(s) and background. The main objective in a supervised detection (also called "target detection") is to search for a specific given spectral material (target) in hyperspectral imaging (HSI) where the spectral signature of the target is known a priori from laboratory measurements. In addition, the fractional abundance of the target is computed. To achieve this we present two linear unmixing algorithms that recognize targets with known (given) spectral signatures. The CLUN is based on automatic feature extraction from the target's spectrum. These features separate the target from the background. The ROTU algorithm is based on embedding the spectra space into a special space by random orthogonal transformation and on the statistical properties of the embedded result. Experimental results demonstrate that the targets' locations were extracted correctly and these algorithms are robust and efficient.

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<title>N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data</title>

Cited by 1,305 publications

References 0 publications

A sparse semi-blind source identification method and its application to Raman spectroscopy for explosives detection

A sparse semi-blind source identification method and its application to Raman spectroscopy for explosives detection

Unsupervised Bayesian analysis of gene expression patterns

Two Linear Unmixing Algorithms to Recognize Targets Using Supervised Classification and Orthogonal Rotation in Airborne Hyperspectral Images

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