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Tools for Full Spectrum Analysis of Hyperspectral Data--Technical Research Associates, Inc., 3602 Woodlawn Drive, Honolulu, HI 96822; 858‑926‑7179; http://www.stormingmedia.us
Dr. Michael E. Winter, Principal Investigator, winter@higp.hawaii.edu
Dr. Edwin M. Winter, Business Official, edwinter@tracam.com
DOE Grant No. DE‑FG02‑06ER84642
Amount: $748,463
Hyperspectral sensor systems are
currently under consideration for applications related to monitoring the
nuclear fuel cycle and other signatures of interest to the nonproliferation
community. These applications include
the detection of camouflaged and concealed targets, gas plume detection and
identification, and terrain classification.
Unfortunately, many of the data exploitation tools are not user-friendly
and do not fully exploit all the spectral data available. For example, hyperspectral
data from different frequency bands are analyzed separately. This project will develop technology to allow
the analysis of the full spectrum, thereby increasing the probability of proper
identification and reducing false alarms.
During Phase I, the requirements were analyzed and several segmentation techniques
– Independent Component Analysis (ICA) and Non-Negative Matrix Factorization
(NNMF) – were implemented. In addition,
the extension of the current Microcorder-rule-based
spectral identification algorithm to the long wave infrared (LWIR) was
investigated using data collected simultaneously by
visible-near-infrared/short-wave-infrared (VNIR/SWIR) and LWIR hyperspectral
sensors. In Phase II, software tools for
the full spectrum analysis of hyperspectral data will be developed. Besides
Commercial Applications and Other Benefits as described by the awardee: The software package should find application in the detection of contaminants, military target detection, and exploration geology. With respect to the detection of contaminants, bringing together the reflection and thermal bands would offer the possibility of discriminating against false alarm sources. For geological remote sensing, the technology could overcome the restrictions of using only the reflection-dominated portions of the spectrum, in which certain key minerals of economic importance are missed.