### Abstract

Hyperspectral data is modeled as an unknown mixture of original features (such as the materials present in the scene). The goal is to find the unmixing matrix and to perform the inversion in order to recover them. Unlike first and second order techniques (such as PCA), higher order statistics (HOS) methods assume the data has nongaussian behavior are able to represent much subtle differences among the original features. The HOS algorithms transform the data such that the result components are uncorrelated and their nongaussianity is maximized (the resulting components are statistical independent). Subpixel targets in a natural background can be seen as anomalies of the image scene. They expose a strong nongaussian behavior and correspond to independent components leading to their detection when HOS techniques are employed. The methods presented in this paper start by preprocessing the hyperspectral image through centering and sphering. The resulting bands are transformed using gradient-based optimization on the HOS measure. Next, the data are reduced through a selection of the components associated with small targets using the changes of the slope in the scree graph of the non-Gaussianity values. The targets are filtered using histogram-based analysis. The end result is a map of the pixels associated with small targets.

Original language | English |
---|---|

Article number | 42 |

Pages (from-to) | 424-431 |

Number of pages | 8 |

Journal | Proceedings of SPIE - The International Society for Optical Engineering |

Volume | 5674 |

DOIs | |

State | Published - 20 Jul 2005 |

Event | Proceedings of SPIE-IS and T Electronic Imaging - Computational Imaging III - San Jose, CA, United States Duration: 17 Jan 2005 → 18 Jan 2005 |

### Fingerprint

### Keywords

- Higher order statistics
- Hyperspectral imagery
- Independent Component Analysis
- Target detection

### Cite this

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**Subpixel target detection in hyperspectral data using higher order statistics source separation algorithms.** / Robila, Stefan.

Research output: Contribution to journal › Conference article › Research › peer-review

TY - JOUR

T1 - Subpixel target detection in hyperspectral data using higher order statistics source separation algorithms

AU - Robila, Stefan

PY - 2005/7/20

Y1 - 2005/7/20

N2 - Hyperspectral data is modeled as an unknown mixture of original features (such as the materials present in the scene). The goal is to find the unmixing matrix and to perform the inversion in order to recover them. Unlike first and second order techniques (such as PCA), higher order statistics (HOS) methods assume the data has nongaussian behavior are able to represent much subtle differences among the original features. The HOS algorithms transform the data such that the result components are uncorrelated and their nongaussianity is maximized (the resulting components are statistical independent). Subpixel targets in a natural background can be seen as anomalies of the image scene. They expose a strong nongaussian behavior and correspond to independent components leading to their detection when HOS techniques are employed. The methods presented in this paper start by preprocessing the hyperspectral image through centering and sphering. The resulting bands are transformed using gradient-based optimization on the HOS measure. Next, the data are reduced through a selection of the components associated with small targets using the changes of the slope in the scree graph of the non-Gaussianity values. The targets are filtered using histogram-based analysis. The end result is a map of the pixels associated with small targets.

AB - Hyperspectral data is modeled as an unknown mixture of original features (such as the materials present in the scene). The goal is to find the unmixing matrix and to perform the inversion in order to recover them. Unlike first and second order techniques (such as PCA), higher order statistics (HOS) methods assume the data has nongaussian behavior are able to represent much subtle differences among the original features. The HOS algorithms transform the data such that the result components are uncorrelated and their nongaussianity is maximized (the resulting components are statistical independent). Subpixel targets in a natural background can be seen as anomalies of the image scene. They expose a strong nongaussian behavior and correspond to independent components leading to their detection when HOS techniques are employed. The methods presented in this paper start by preprocessing the hyperspectral image through centering and sphering. The resulting bands are transformed using gradient-based optimization on the HOS measure. Next, the data are reduced through a selection of the components associated with small targets using the changes of the slope in the scree graph of the non-Gaussianity values. The targets are filtered using histogram-based analysis. The end result is a map of the pixels associated with small targets.

KW - Higher order statistics

KW - Hyperspectral imagery

KW - Independent Component Analysis

KW - Target detection

UR - http://www.scopus.com/inward/record.url?scp=21944440949&partnerID=8YFLogxK

U2 - 10.1117/12.586971

DO - 10.1117/12.586971

M3 - Conference article

VL - 5674

SP - 424

EP - 431

JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

SN - 0277-786X

M1 - 42

ER -