We postulate an optical configuration which takes a multispectral/hyperspectral scene and collects a multiplexed spectral sample on the Focal Plane Array (FPA). From such a measurement paradigm, the data is then processed with compressive imaging techniques and we recover the full multispectral cube from a single frame of imagery. We use a trained dictionary prior assumption along with a greedy reconstruction algorithm for local multispectral reconstruction.
In this paper we describe the algorithm for local image reconstructions from global measurements on the Focal Plane Array (FPA). The global measurements may come from a multiplexed imaging and /or convolution-based sampling model. The algorithm consists of scanning a rectangular segment on the FPA data and reconstructing the image on that segment using a modified Wiener Filter by adapting the measurements on the data via a linear operator. This method is essential in the reconstruction of large format images from large data samples. In particular, in this paper the method is applied to multiplexed, multispectral imaging from a single measurement on the FPA.
The excitation-emission matrix (EEM) is the luminescence spectral emission intensity of fluorescent compounds as a function of the excitation wavelength. EEMs offer the promise of an additional degree of information for enhanced compound detection and identification. Veridian has collected pure-component EEMs of amino acids (Trp, Phe, Tyr), Bacillus globigii (bg), Bacillus thuringiensis (bt,), and selected backgrounds. Also collected were EEMs of mixtures of amino acids and of bg in solution with a few backgrounds. The EEMs of pure components and mixtures were analyzed for phenomenology and for potential methods of unmixing and identifying the constituents of EEMs having mixed components of a similar nature.
KEYWORDS: Sensors, Optical filters, Detection and tracking algorithms, Data processing, Chemical elements, Diamond, Matrices, Vegetation, Signal detection, Detector arrays
Many spectral signature detection algorithms depend on numerically inverting covariance matrices. Hyperspectral data rarely span the full band space because of factors such as sensor noise, numerical round-off, sparse sampling, and band correlation inherent in the data or introduced by data processing. Processing the full order of the covariance matrix without regard to its useful rank leads to reduced detection performance. It was previously shown that the performance of inverse-covariance based detection algorithms can be improved by regularizing the covariance matrix inversion through extension of an optimally chosen eigenvalue. The extension method provides a robust way to optimize signal to clutter ratio (SCR) on data collected with a detector of uniform gain. The method of trusted eigenvalue extension has now been applied to data collected with a sensor with multiple gain regions. Multiple gain regions are used on wide spectral range sensors such as HYDICE and complicate the inversion of the covariance matrix over the full range of spectral bands. Further optimization of the trusted eigenvalue is presented and compared against traditional regularization methods. Since the extension method is particularly intended for sparsely sampled data with high dimensionality, a comparison is presented between the extension method and band coaddition.
KEYWORDS: Optical filters, Calibration, Digital filtering, Linear filtering, Matrices, Single mode fibers, Imaging systems, Sensors, Data processing, Distance measurement
Hyperspectral data rarely spans the full band space because of factors such as sensor noise, numerical round-off, sparse sampling, and band correlation introduced by data processing. Standard exploitation of data, which often does not consider the possibility of a reduced band space, leads to reduced detection performance. Spectral signature detection performance can be improved by estimating the covariance on a subset of the band space components. The decision about how to limit the band space can be determined by factors such as in-scene estimation of noise. In-scene estimation of noise can be used to optimize spectral signature detection when spectral filtering methods based on covariance inverses are used. We present here a method for determining instrument noise and a new method of covariance inverse regularization which increases spectral filtering performance.
The objective of the US Army Hyperspectral Mine Detection Phenomenology program was to determine if spectral discriminants exist that are useful for the detection of land mines. Statistically significant mine signature data were collected over a wide spectral range and analyzed to identify robust spectral features that might serve as discriminants for new airborne sensor concepts. Detection metrics which characterize the deductibility of land miens and which predict the detection performance of a general class of hyperspectral detection algorithms were selected and applied. Detection performance of land mines was analyzed against background type, age of buried miens and possible sensor design parameters. This paper describes the result of this analysis and present EO/IR hyperspectral sensor and algorithm design concepts that could potentially be used to operationally detect buried land mines.
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