Publications

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Low signal-to-noise data processing algorithms for improved detection, tracking, discrimination and situational threat assessment are a key research challenge. As sensor technologies progress, the number of pixels will increase signi cantly. This will result in increased resolution, which could improve object discrimination, but unfortunately, will also result in a significant increase in the number of potential targets to track. Many tracking techniques, like multi-hypothesis trackers, suffer from a combinatorial explosion as the number of potential targets increase. As the resolution increases, the phenomenology applied towards detection algorithms also changes. For low resolution sensors, "blob" tracking is the norm. For higher resolution data, additional information may be employed in the detection and classfication steps. The most challenging scenarios are those where the targets cannot be fully resolved, yet must be tracked and distinguished for neighboring closely spaced objects. Tracking vehicles in an urban environment is an example of such a challenging scenario. This report evaluates several potential tracking algorithms for large-scale tracking in an urban environment.

Spatially unresolved targets, such as vehicles, reflect a radiance spectrum that is more complicated than the simple linear mixing of target and background material spectra. Although different materials in the target and background classes have Bi-Directional Reflectance Function (BRDF) dependent spectra, the unique geometry and orientation of a target object, in addition to the solar illumination and observation angles, define a more complex Tri-Directional Reflectance Function (TRDF) in which glints and shadows are important spectral contributors. For different observation scenarios, the apparent spectra of an unresolved target may vary significantly. However, since solar and observation angles are often known to operators of remote sensing instruments, well characterized TRDFs for specific targets allow for refinement in the estimation of the expected spectra of different unresolved targets. More accurately defined target classes may lead to improved performance in established subpixel target detection algorithms for remote sensing.

Remote detection of radiation is a difficult problem due to the 1/r 2 fall-off Recent advances in polymer research and nanoscale fabrication methods along with advances in optical Polarimetrie remote sensing systems suggest a solution. The basic device uses a micro-wiregrid infrared polarizer fabricated in conductive polymer. When the polymer is exposed to hard radiation, its conductivity will be affected and the polarization properties of the device will change in a corresponding manner. This change in polarization properties can be determined by optically interrogating the device, possibly from a remote location. We will report on the development of a radiation-sensitive passive dosimeter polymer with very low optical visibility. Progress on material development, lithographic fabrication and optical characterization will be presented.

This paper reports on a novel approach to atmospheric cloud segmentation from a space based multi-spectral pushbroom satellite system. The satellite collects 15 spectral bands ranging from visible, 0.45 urn, to long wave in fared (IR), 10.7um. The images are radiometrically calibrated and have ground sample distances (GSD) of 5 meters for visible to very near IR bands and a GSD of 20 meters for near IR to long wave IR. The algorithm consists of a hybrid-classification system in the sense that supervised and unsupervised networks are used in conjunction. For performance evaluation, a series of numerical comparisons to human derived cloud borders were performed. A set of 33 scenes were selected to represent various climate zones with different land cover from around the world. The algorithm consisted of the following. Band separation was performed to find the band combinations which form significant separation between cloud and background classes. The potential bands are fed into a K-Means clustering algorithm in order to identify areas in the image which have similar centroids. Each cluster is then compared to the cloud and background prototypes using the Jeffries-Matusita distance. A minimum distance is found and each unknown cluster is assigned to their appropriate prototype. A classification rate of 88% was found when using one short wave IR band and one midwave IR band. Past investigators have reported segmentation accuracies ranging from 67% to 80%, many of which require human intervention. A sensitivity of 75% and specificity of 90% were reported as well.

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The Multispectral Thermal Imager Satellite (MTI) has been used to test a sub-pixel sampling technique in an effort to obtain higher spatial frequency imagery than that of its original design. The MTI instrument is of particular interest because of its infrared detectors. In this spectral region, the detector size is traditionally the limiting factor in determining the satellite's ground sampling distance (GSD). Additionally, many over-sampling techniques require flexible command and control of the sensor and spacecraft. The MTI sensor is well suited for this task, as it is the only imaging system on the MTI satellite bus. In this super-sampling technique, MTI is maneuvered such that the data are collected at sub-pixel intervals on the ground. The data are then processed using a deconvolution algorithm using in-scene measured point spread functions (PSF) to produce an image with synthetically-boosted GSD.