Focal plane arrays with associated electronics and cooling are a substantial portion of the cost, complexity, size, weight,
and power requirements of Long-Wave IR (LWIR) imagers. Hyperspectral LWIR imagers add significant data volume
burden as they collect a high-resolution spectrum at each pixel. We report here on a LWIR Hyperspectral Sensor that
applies Compressive Sensing (CS) in order to achieve benefits in these areas.
The sensor applies single-pixel detection technology demonstrated by Rice University. The single-pixel approach uses a
Digital Micro-mirror Device (DMD) to reflect and multiplex the light from a random assortment of pixels onto the
detector. This is repeated for a number of measurements much less than the total number of scene pixels. We have
extended this architecture to hyperspectral LWIR sensing by inserting a Fabry-Perot spectrometer in the optical path.
This compressive hyperspectral imager collects all three dimensions on a single detection element, greatly reducing the
size, weight and power requirements of the system relative to traditional approaches, while also reducing data volume.
The CS architecture also supports innovative adaptive approaches to sensing, as the DMD device allows control over the
selection of spatial scene pixels to be multiplexed on the detector.
We are applying this advantage to the detection of plume gases, by adaptively locating and concentrating target energy.
A key challenge in this system is the diffraction loss produce by the DMD in the LWIR. We report the results of testing
DMD operation in the LWIR, as well as system spatial and spectral performance.
Effective application of point detectors in the field to monitor the air for biological attack imposes a challenging set of requirements on threat detection algorithms. Raman spectra exhibit features that discriminate between threats and non-threats, and such spectra can be collected quickly, offering a potential solution given the appropriate algorithm. The algorithm must attempt to match to known threat signatures, while suppressing the background clutter in order to produce acceptable Receiver Operating Characteristic (ROC) curves. The radar space-time adaptive processing (STAP) community offers a set of tools appropriate to this problem, and these have recently crossed over into hyperspectral imaging (HSI) applications. The Adaptive Subspace Detector (ASD) is the Generalized Likelihood Ratio Test (GLRT) detector for structured backgrounds (which we expect for Raman background spectra) and mixed pixels, and supports the necessary adaptation to varying background environments. The structured background model reduces the training required for that adaptation, and the number of statistical assumptions required. We applied the ASD to large Raman spectral databases collected by ChemImage, developed spectral libraries of threat signatures and several backgrounds, and tested the algorithm against individual and mixture spectra, including in blind tests. The algorithm was successful in detecting threats, however, in order to maintain the desired false alarm rate, it was necessary to shift the decision threshold so as to give up some detection sensitivity. This was due to excess spread of the detector histograms, apparently related to variability in the signatures not captured by the subspaces, and evidenced by non-Gaussian residuals. We present here performance modeling, test data, algorithm and sensor performance results, and model validation conclusions.
Satellite observations of atmospheric CO2 are the key to answering important questions regarding spatial and temporal variabilities of carbon sources and sinks. Global measurements sampling the air above land and oceans allow oceanic flux to be distinguished from terrestrial flux. Continuous sampling on frequent basis allows seasonal variations to be distinguished. This study quantifies the potential value of satellite-based measurements of column- integrated CO2 concentrations in terms of the carbon source/sink information that can be derived from these concentrations via inverse modeling. We discuss the utility of the carbon flux inversions in terms of both spatial and temporal resolution, compare capabilities of active and passive approaches to the measurements, and demonstrate the feasibility of high precision CO2 column concentration retrievals.
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