Craig R. Schwartz

Adaptive multispectral CFAR detection of land mines

An automatic target detection algorithm which exploits spectral and spatial signatures of mines is described. Key features of this approach include the ability to adapt to unknown or changing background statistics and the capability to operate with unknown spectral signatures. Preliminary results of applying this algorithm for surface mine detection in video-based multispectral imagery covering the 400-900 nm region are presented. Tests on actual airborne data collected during 1992, 1993, and 1994 show that at 8-inch ground resolution (with 4x over-sampling), 12-inch diameter circular mines can be discriminated from natural backgrounds with a probability of detection around 85% with 3-4 false alarms per image in a relatively harsh clutter environment. This capability has been shown to be sufficient to meet COBRA minefield requirements during preliminary system testing.

Comparison of infrared imaging hyperspectral sensors for military target detection applications

Recent studies have demonstrated the potential for exploring spectral discriminates in the thermal infrared for day/night surveillance and targeting of military targets in situations where the thermal contrast is low. Although the spectral discriminates have been found to be very subtle in most cases, good detection performance is achievable due to the generally high band-to-band spectral correlation of the background. This, however, presents a challenging set of requirements for infrared multispectral and hyperspectral sensors designed for this application. In this paper, we examine the merits and limitations of various design approaches, including imaging Michelson interferometers, dispersive spectrometers, and spatial Fourier transform spectrometers. The comparison is based on detailed sensor modeling as well as laboratory and field measurements of state-of-the-art instruments: a dispersive spectrometers and a n imaging Fourier transform spectrometer. The primary emphasis of this paper is the design of a hyperspectral sensor for tower-based and subsequent airborne data collection. Implications for operational multispectral sensor designs are also given.

Focal plane array nonlinearity and nonuniformity impacts to target detection with thermal infrared imaging spectrometers

Recent studies have demonstrated the potential for spectrally discriminating low contrast ground-based military targets in the thermal infrared for day/night reconnaissance, surveillance, and targeting applications. Although the underlying spectral features have been found to be very subtle in most cases, good detection performance is achievable due to the generally high band-to-band spectral correlation of terrestrial backgrounds. Recently, attempts have been made to develop imaging spectrometers of sufficient quality to preserve this high background spectral correlation and, in the process, provide robust target detection capabilities. One key issue which must be addressed in the sensor design is the impact of focal plane nonlinearity and nonuniformity on spectral correlation. In this paper, we present the details of a Monte-Carlo model which was developed to quantify this impact as a function of focal plane array characteristics for three sensing modalities: a dispersive spectrometer, a temporal Fourier transform spectrometer, and a spatial Fourier transform spectrometer. The results illustrate distinct differences in how these focal plane error sources propagate into the spectral domain and perturb the measured spectral statistics.

Thermal multispectral detection of military vehicles in vegetated and desert backgrounds

Recent data collections using an infrared hyperspectral measurement system have provided a significant measurement database of military vehicles in vegetated and desert backgrounds. This paper summarizes the results of a study performed to assess the detection performance potential of multispectral sensors using this database. Specific issues addressed include approaches to optimal band selection; robustness of band combinations with target, background, and environment diversity; and sensor noise requirements. All of these issues are vital to assessing the feasibility and utility of infrared multispectral sensors in operational scenarios.

Statistical parametric signature/sensor/detection model for multispectral mine target detection

A statistical parametric multispectral sensor performance model was developed by ERIM to support mine field detection studies, multispectral sensor design/performance trade-off studies, and target detection algorithm development. The overall model incorporates four components; a mission flight model, a multispectral target and background signature model, a multispectral sensor model, and a multispectral target detection model. Emphasis is placed on estimating the effects of mission multispectral target detection algorithms. Thus, the model ideally supports mission and multispectral sensor trade studies which require optimization of the systems overall target detection performance. The model and a typical example of performance prediction results are presented.

Analysis of infrared multispectral target/background field measurements

Thermal infrared multi-spectral field measurements of test panels, military vehicles, and backgrounds were extensively analyzed to assess the potential of multi-spectral processing for detecting low-contrast ground targets in vegetation clutter. The measurements clearly show the existence of exploitable color due to fine-scale variations in target-background spectral contrast, and they establish environment limits on coherent multi-band clutter suppression based on background spectral correlation. Typical variations in key multi-spectral performance parameters, and their implications for waveband selection, sensor design, and robust target detection performance, are presented and discussed.

Infrared multispectral target/background field measurements

Extensive measurements of targets and backgrounds were made in the field by an infrared Fourier transform spectrometer. These measurements were made to provide statistically valid estimates of target to background spectral contrast and background spatial and spectral statistics to support the use of multispectral sensing techniques for detecting military targets in clutter. The details of the spectrometer, the targets, the backgrounds, and the measurements are given.

Detection of land mines with hyperspectral data

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.

Target detection in desert backgrounds: infrared hyperspectral measurements and analysis

Infrared multispectral sensors are being investigated as a means for day and night target detection. Infrared multispectral sensors would exploit high spectral band-to-band correlation to reject high background clutter. An infrared Fourier transform spectrometer-based field measurement system was used to collect spectral signature data of targets and desert scrub and sand backgrounds from a 100 foot tower at White Sands Missile Range. The measurements include target-to-background spectral contrast, subpixel targets, background spectral correlation, and background spatial power spectra. The measurements have been analyzed to determine multispectral signal-to-clutter ratios versus target, background, diurnal, and weather variations, background correlation versus temperature clutter variations, and spectral correlation versus spatial scale. These measurements contribute to the expanding target and background infrared hyperspectral signature database. The results of the analysis demonstrate the utility and robustness of infrared multispectral techniques for target detection.

Comprehensive hyperspectral system simulation: II. Hyperspectral sensor simulation and premliminary VNIR testing results

An end-to-end hyperspectral system model with applications to space and airborne sensor platforms is under development and testing. In this paper we discuss current work in the development of the sensor model and the results of preliminary testing. It is capable of simulating collected hyperspectral imagery of the ground as sensors operating from space or airborne platforms would acquire it. Dispersive hyperspectral imaging sensors operating from the visible through the thermal infrared spectral regions can be modeled with actual hyperspectral imagery or simulated hyperspectral scenes used as inputs. In the sensor model portion, fore-optics (misalignment), dispersive spectrometer designs, degradations (platform motion, smile, keystone, misregistration), focal plane array (temperature drift, nonuniformity/nonlinearity), noise (shot, dark, Johnson, 1/f, RMS read, excess low frequency), analog-to-digital conversion, digital processing, and radiometric/temporal/wavelength calibration effects are included. The overall model includes a variety of processing algorithms including constant false alarm rate anomaly detection, spectral clustering of backgrounds for anomaly detection, atmospheric compensation, and pairwise adaptive linear matching for detection and classification. Results of preliminary testing using synthetic scene data in the visible/near infrared portion of the spectrum are discussed. Potential applications for this modeling capability include processing results performance prediction and sensor parameter specification trade studies.