Feature Transformation Detection Method with Best Spectral Band Selection Process for Hyper-spectral Imaging

We present a newly developed feature transformation (FT) detection method for hyper-spectral imagery (HSI) sensors. In essence, the FT method, by transforming the original features (spectral bands) to a different feature domain, may considerably increase the statistical separation between the target...

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Bibliographic Details
Published in:Sensing and imaging 2015-12, Vol.16 (1), Article 11
Main Authors: Chen, Hai-Wen, McGurr, Mike, Brickhouse, Mark
Format: Article
Language:English
Subjects:
Electrical Engineering
Engineering
Hyperspectral Imaging and Image Processing
Imaging
Microwaves
Original Paper
Radiology
RF and Optical Engineering
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Summary:We present a newly developed feature transformation (FT) detection method for hyper-spectral imagery (HSI) sensors. In essence, the FT method, by transforming the original features (spectral bands) to a different feature domain, may considerably increase the statistical separation between the target and background probability density functions, and thus may significantly improve the target detection and identification performance, as evidenced by the test results in this paper. We show that by differentiating the original spectral, one can completely separate targets from the background using a single spectral band, leading to perfect detection results. In addition, we have proposed an automated best spectral band selection process with a double-threshold scheme that can rank the available spectral bands from the best to the worst for target detection. Finally, we have also proposed an automated cross-spectrum fusion process to further improve the detection performance in lower spectral range (
ISSN:1557-2064
1557-2072
DOI:10.1007/s11220-015-0113-4