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

Content Provider	Springer Nature Link
Author	Chen, Hai Wen McGurr, Mike Brickhouse, Mark
Copyright Year	2015
Abstract	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 (<1000 nm) by selecting the best spectral band pair with multivariate analysis. Promising detection performance has been achieved using a small background material signature library for concept-proving, and has then been further evaluated and verified using a real background HSI scene collected by a HYDICE sensor.
Starting Page	1
Ending Page	33
Page Count	33
File Format	PDF
ISSN	15572064
Journal	Sensing and Imaging: An International Journal
Volume Number	16
Issue Number	1
e-ISSN	15572072
Language	English
Publisher	Springer US
Publisher Date	2015-07-05
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Hyper-spectral imagery (HSI) Double-threshold scheme Feature transformation (FT) detection method Automated best spectral band and fused band-pair selection process ROC curve estimation Electrical Engineering Microwaves, RF and Optical Engineering Imaging Radiology
Content Type	Text
Resource Type	Article
Subject	Instrumentation Electrical and Electronic Engineering

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