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Remote Sens. 2012, 4(2), 532-560; doi:10.3390/rs4020532
Article

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

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Received: 5 December 2011; in revised form: 31 January 2012 / Accepted: 1 February 2012 / Published: 21 February 2012
(This article belongs to the Special Issue Hyperspectral Remote Sensing)
Abstract: 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.
Keywords: hyperspectral imaging; unmixing; spectral signature; target recognition; sub-above pixel; supervised classification hyperspectral imaging; unmixing; spectral signature; target recognition; sub-above pixel; supervised classification
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Averbuch, A.; Zheludev, M. Two Linear Unmixing Algorithms to Recognize Targets Using Supervised Classification and Orthogonal Rotation in Airborne Hyperspectral Images. Remote Sens. 2012, 4, 532-560.

AMA Style

Averbuch A, Zheludev M. Two Linear Unmixing Algorithms to Recognize Targets Using Supervised Classification and Orthogonal Rotation in Airborne Hyperspectral Images. Remote Sensing. 2012; 4(2):532-560.

Chicago/Turabian Style

Averbuch, Amir; Zheludev, Michael. 2012. "Two Linear Unmixing Algorithms to Recognize Targets Using Supervised Classification and Orthogonal Rotation in Airborne Hyperspectral Images." Remote Sens. 4, no. 2: 532-560.


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