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A quasi-Newton-based spatial multiple materials detector for hyperspectral imagery

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Abstract

Hyperspectral remote sensing in space provides information related to surface material characteristics of spacecraft or planets that can be exploited to perform automated detection of targets of interest. At present, the detection in space environment is definitely a hot spot all over the world. So, developing the technique of multiple materials detection makes great sense. In this paper, we propose an algorithm for spatial multiple materials detection in hyperspectral images, which is based on high-order statistics and quasi-Newton method. The proposed detection algorithm, quasi-Newton-based multiple materials detector (QNMMD), exploits spectral information exclusively to make decisions by considering that each pixel contains the interesting materials or not. After single time detection, the pixel containing multiple interesting materials spectra can be exactly detected. The proposed detector has three superiorities. Firstly, due to the quasi-Newton method the proposed algorithm is relatively fast. It needs few times iteration for detecting calculation. Secondly, it performs well when the interesting materials are in low probabilities or small population with the non-Gaussian statistics. Thirdly, with regularization items the algorithm is robust to noise and works well when there are various kinds of interesting materials needing to be detected. Experimental results based on the hyperspectral image of Hubble Space Telescope prove the QNMMD algorithm is effective.

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Acknowledgments

The work was supported by the National Natural Science Foundation of China under the Grants 60975003 and 91120301, the 973 Program under the Grant 2010CB327904, the Fundamental Research Funds for the Central Universities under the Grants YWF-10-01-A10 and YWF-11-03-Q-066, the Beijing Natural Science Foundation (Non-negative Component Analysis for Hyperspectral Imagery Unmixing) under the Grant 4112036, and Program for New Century Excellent Talents in University of Ministry of Education of China under the Grant NCET-11-0775. We are also grateful to Beijing Key Laboratory of Digital Media, Beihang University.

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  1. Image Processing Center, School of Astronautics, Beihang University, Beijing, 100191, People’s Republic of China

    Zhen Qin, Zhenwei Shi & Zhiguo Jiang

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  1. Zhen Qin
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  2. Zhenwei Shi
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  3. Zhiguo Jiang
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Correspondence to Zhenwei Shi.

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Qin, Z., Shi, Z. & Jiang, Z. A quasi-Newton-based spatial multiple materials detector for hyperspectral imagery. Neural Comput & Applic 23, 403–409 (2013). https://doi.org/10.1007/s00521-012-0932-3

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  • DOI: https://doi.org/10.1007/s00521-012-0932-3

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