Robert J. Sherwood

Extended-image reconstruction through horizontal path turbulence using bispectral speckle interferometry

Results are presented from a horizontal path imaging experiment in which a 0.5-m telescope was focused on targets located at a range of 1 .2 km. The targets varied in complexity from simple binary letters to extended representations of satellites with gray scale and size variations. Imaging at a center wavelength of 0.7 μm, we found an atmospheric degradation factor of D / r 0 = 17, on average. We used a slow read-rate bare CCD detector and thus had to deal effectively with additive noise in the speckle measurements. Our image reconstruction algorithms are based on the use of the complex bispectrum, and we have demonstrated diffraction-limited imaging down to light levels approaching a few photons per speckle per resolution area. We have paid careful attention to the effects of additive noise on the reconstruction process and have shown that they can be adequately overcome. These results support the feasibility of high-resolution speckle imaging of high-earth-orbit satellites using CCDs.

Detecting buried objects by fusing dual-band infrared images

The authors have conducted experiments to demonstrate the enhanced detectability of buried land mines using sensor fusion techniques. Multiple sensors, including visible imagery, infrared imagery, and ground penetrating radar (GPR), have been used to acquire data on a number of buried mines and mine surrogates. Because the visible wavelength and GPR data are currently incomplete, the paper focuses on the fusion of two-band infrared images. The authors use feature-level fusion and supervised learning with the probabilistic neural network (PNN) to evaluate detection performance. The novelty of the work lies in the application of advanced target recognition algorithms, the fusion of dual-band infrared images and evaluation of the techniques using two real data sets.<<ETX>>

Sensor feature fusion for detecting buried objects

Given multiple registered images of the earths surface from dual-band infrared sensors, our system fuses information from the sensors to reduce the effects of clutter and improve the ability to detect buried or surface target sites. The sensor suite currently includes two infrared sensors (5 micron and 10 micron wavelengths) and one ground penetrating radar (GPR) of the wide-band pulsed synthetic aperture type. We use a supervised learning pattern recognition approach to detect metal and plastic land mines buried in soil. The overall process consists of four main parts: preprocessing, feature extraction, feature selection, and classification. We present results of experiments to detect buried land mines from real data, and evaluate the usefulness of fusing feature information from multiple sensor types, including dual-band infrared and ground penetrating radar. The novelty of the work lies mostly in the combination of the algorithms and their application to the very important and currently unsolved operational problem of detecting buried land mines from an airborne standoff platform.

Buried object remote detection technology for law enforcement

A precise airborne temperature-sensing technology to detect buried objects for use by law enforcement is developed. Demonstrations have imaged the sites of buried foundations, walls and trenches; mapped underground waterways and aquifers; and been used to locate underground military objects. The methodology is incorporated in a commercially available, high signal-to-noise, dual-band infrared scanner with real-time, 12-bit digital image processing software and display. The method creates color-coded images based on surface temperature variations of 0.2 degree(s)C. Unlike other less-sensitive methods, it maps true (corrected) temperatures by removing the (decoupled) surface emissivity mask equivalent to 1 degree(s)C or 2 degree(s)C; this mask hinders interpretation of apparent (blackbody) temperatures. Once removed, it is possible to identify surface temperature patterns from small diffusivity changes at buried object sites which heat and cool differently from their surroundings. Objects made of different materials and buried at different depths are identified by their unique spectral, spatial, thermal, temporal, emissivity and diffusivity signatures. The authors have successfully located the sites of buried (inert) simulated land mines 0.1 to 0.2 m deep; sod-covered rock pathways alongside dry ditches, deeper than 0.2 m; pavement covered burial trenches and cemetery structures as deep as 0.8 m; and aquifers more than 6 m and less than 60 m deep. The technology could be adapted for drug interdiction and pollution control. For the former, buried tunnels, underground structures built beneath typical surface structures, roof-tops disguised by jungle canopies, and covered containers used for contraband would be located. For the latter, buried waste containers, sludge migration pathways from faulty containers, and the juxtaposition of groundwater channels, if present, nearby, would be depicted. The precise airborne temperature-sensing technology has a promising potential to detect underground epicenters of smuggling and pollution.

Dual-band infrared capabilities for imaging buried object sites

We discuss dual-band infrared (DBIR) capabilities for imaging buried object sites. We identify physical features affecting thermal contrast needed to distinguish buried object sites from undisturbed sites or surface clutter. Apart from atmospheric transmission and system performance, these features include: object size, shape, and burial depth; ambient soil, disturbed soil and object site thermal diffusivity differences; surface temperature, emissivity, plant-cover, slope, albedo and roughness variations; weather conditions and measurement times. We use ground instrumentation to measure the time-varying temperature differences between buried object sites and undisturbed soil sites. We compared near surface soil temperature differences with radiometric infrared (IR) surface temperature differences recorded at 4.7 +/- 0.4 micrometers and at 10.6 +/- 1.0 micrometers . By producing selective DBIR image ratio maps, we distinguish temperature-difference patterns from surface emissivity effects. We discuss temperature differences between buried object sites, filled hole sites (without buried objects), cleared (undisturbed) soil sites, and grass-covered sites (with and without different types of surface clutter). We compared temperature, emissivity-ratio, visible and near-IR reflectance signatures of surface objects, leafy plants and sod. We discuss the physical aspects of environmental, surface and buried target features affecting interpretation of buried targets, surface objects and natural backgrounds.

Computer vision for locating buried objects

Given two registered images of the Earth, measured with aerial dual-band infrared (IR) sensors, the authors use advanced computer vision/automatic target recognition techniques to estimate the positions of buried land mines. The images are very difficult to interpret, because of large amounts of clutter. Conventional techniques use single-band imagery and simple correlations. They rely heavily on the judgment of the human doing the interpretation, and give unsatisfactory results with difficult data sets of the type analyzed here. The automatic algorithms used by the authors are able to eliminate most of the clutter and give greatly improved indications of regions in the image that could be interpreted as mines. The novelty of the present approach lies in the following aspects: (1) a patented data fusion technique using two IR images and physical principles based on Plancks law; (2) a new region-based texture segmentation algorithm using Gabor transform features and a clustering/thresholding algorithm based on a neural network (self-organizing feature map); (3) prior knowledge of measured feasible temperatures and emissivities; and (4) results with real data using buried surrogate mines.<<ETX>>

Data fusion for the detection of buried land mines

We have conducted experiments to demonstrate the enhanced detectability of buried land mines using sensor fusion techniques. Multiple sensors, including visible imagery, IR imagery, and ground penetrating radar, have been used to acquire data on a number of buried mines and mine surrogates. We present this data along with a discussion of our application of sensor fusion techniques for this particular detection problem. We describe our data fusion architecture and discuss the some relevant results of these classification methods.

Computer vision and sensor fusion for detecting buried objects

Given multiple images of the Earths surface from dual-band infrared sensors, a system that fuses information from the sensors to reduce the effects of clutter and improve the ability to detect buried or surface target sites is presented. Supervised learning pattern classifiers (including neural networks) are used. Results of experiments to detect buried land mines from real data are given, and the usefulness of fusing information from multiple sensor types is evaluated. The novelty of the work lies mostly in the combination of the algorithms and their application to the very important and currently unsolved problem of detecting buried land mines from an airborne standoff platform.<<ETX>>

Experimental validation of extended image reconstruction using bispectral speckle interferometry

Results will be presented from a horizontal path imaging experiment in which we used a 0.5 meter telescope focused on a target located at a range of 1 .2 km. The targets included various extended objects from simple binary letters to extended representations of satellites with grey scale and size variations. Imaging at a center wavelength of 0.7 microns, we found an atmospheric degradation factor of Dir0 = 17, on average. We used a slow read-rate bare CCD detector and thus had to effectively deal with additive noise in the speckle measurements. Our image reconstruction algorithms are based on the use of the complex bispectrum and we have demonstrated diffraction-limited imaging down to light levels approaching a few photons per speckle per resolution area. We have paid careful attention to the effects of additive noise on the reconstruction process and shown that they can be adequately overcome.

Simulating speckle interferometry

This paper describes a simulator we have developed to model speckle imaging and resconstruction of astronomical objects. The simulator was designed as a tool in the development of new signal and image processing techniques for our high resolution imaging research. It has been found to accurately replicate the speckle imaging process, and can be used to predict experimental results under various environmental conditions. The simulator is described in detail, including the modeling of atmospheric turbulence effects, the generation of speckle images, and the simulation of the telescope and image detection processes.