An MWIR spectral imaging sensor based on dual direct vision prism (DVP) architecture is described. This sensor represents a third generation of the Chromotomographic Hyperspectral Imaging Sensor (CTHIS). In the new sensor, a direct vision prism is synthesized by the vector addition of the spectral response of two matched, but independently aligned DVP's. The resulting sensor dispersion varies from zero to twice the single prism dispersion, as a function of the
angle between the dispersion axes of the two prisms. The number of resolved channels, and the related signal strength per channel, also adapts with this angle. The "synthesized prism" projects a spectral image onto the focal plane array of an infrared camera. The prism is rotated on the camera axis and the resulting spectral information is employed to form an image cube (x, y, λ), using tomographic techniques. The sensor resolves from 1 to 105 spectral channels, between 3.0μm and 5.2μm wavelength. Spectral image data and image reconstruction is provided for standard test sources and scenes.
In previous conference, we described a powerful class of temporal filters with excellent signal to clutter gains in evolving cloud scenes of consecutive IR sequences. The generic temporal filter is a zero-mean damped sinusoid, implemented recursively. The full algorithm, a triple temporal filter (TTF), consists of a sequence of two zero-mean damped sinusoids followed by an exponential averaging filter. The outputs of the first two filters are weakened at strong local edges. Analysis of a real-world database led to two optimized filters: one dedicated to noise-dominated scenes, the other to cloud clutter-dominated scenes; a dual-channel fusion of the two filters has also been implemented in hardware. This paper describes the post-processing and thresholding of the outputs of the filter algorithms. Post-processing on each output frame is implemented by a simple spatial algorithm which searches for maximum linear or pseudo-linear streaks made up of three linked pixels. The output histogram after post-processing is more robust to histogram- based thresholding and in some cases has improved signal to clutter ratio. The threshold is based on a simple level-occupancy (binary) histogram in which the first gap of 4 empty levels is determined and a threshold established based on this gap value and the number of occupied levels in the histogram above the gap. The post-processing and thresholding of the filter outputs are now operating in real-time hardware. Preliminary flight tests on a small aircraft of the algorithms in real-time operation demonstrate the viability of the approach on a moving platform. Specific examples and a video of the real-time performance on a fixed and moving platform will be presented at the conference.
In an earlier conference, we introduced a powerful class of temporal filters, which have outstanding signal to clutter gains in evolving cloud scenes. The basic temporal filter is a zero-mean damped sinusoid, implemented recursively. Our final algorithm, a triple temporal filter, consists of a sequence of tow zero-mean damped sinusoids followed by an exponential averaging filter along with an edge suppression factor. The algorithm was designed, optimized and tested using a real world database. We applied the Simplex algorithm to a representative subset of our database to find an improved set of filter parameters. Analysis led to two improved filters: one dedicated to benign clutter conditions and the other to cloud clutter-dominated scenes. In this paper, we demonstrate how a fused version of the two optimized filters further improves performance in severe cloud clutter scenes. The performance characteristics of the filters will be detailed by specific examples and plots. Real time operation has been demonstrated on laboratory IR cameras.
We describe a recursive temporal filter based on a running estimate of the temporal variance followed by removal of the baseline variance of each pixel. The algorithm is designed for detection/tracking of 'point' targets moving at sub- pixel/frame velocities, 0.02 to 0.50 p/f, in noise-dominated scenarios on staring IR camera data. The technique responds to targets of either polarity. A preprocessing technique, morphological in origin but implemented by median filters, further improves the S/N sensitivity of the algorithm while restricting the result to positive contrast targets. The computationally simple algorithm has been implemented in hardware and real-time operation is in evaluation. The performance is characterized by some specific examples as well as plots over our extensive database of real data. Detection down to S/N approximately 3 or less and sensitivity to the appropriate range of velocities is demonstrated.