Over the past five years, advances have been made in the spectral detection of surface mines under minefield
detection programs at the U. S. Army RDECOM CERDEC Night Vision and Electronic Sensors Directorate
(NVESD). The problem of detecting surface land mines ranges from the relatively simple, the detection of large
anti-vehicle mines on bare soil, to the very difficult, the detection of anti-personnel mines in thick vegetation.
While spatial and spectral approaches can be applied to the detection of surface mines, spatial-only detection
requires many pixels-on-target such that the mine is actually imaged and shape-based features can be exploited.
This method is unreliable in vegetated areas because only part of the mine may be exposed, while spectral detection
is possible without the mine being resolved. At NVESD, hyperspectral and multi-spectral sensors throughout the
reflection and thermal spectral regimes have been applied to the mine detection problem. Data has been collected
on mines in forest and desert regions and algorithms have been developed both to detect the mines as anomalies and
to detect the mines based on their spectral signature. In addition to the detection of individual mines, algorithms
have been developed to exploit the similarities of mines in a minefield to improve their detection probability. In this
paper, the types of spectral data collected over the past five years will be summarized along with the advances in
Sensor fusion has become a vital research area for mine detection because of the countermine community's conclusion that no single sensor is capable of detecting mines at the necessary detection and false alarm rates over a wide variety of operating conditions. The U. S. Army Night Vision and Electronic Sensors Directorate (NVESD) evaluates sensors and algorithms for use in a multi-sensor multi-platform airborne detection modality. A large dataset of hyperspectral and radar imagery exists from the four major data collections performed at U. S. Army temperate and arid testing facilities in Autumn 2002, Spring 2003, Summer 2004, and Summer 2005. There are a number of algorithm developers working on single-sensor algorithms in order to optimize feature and classifier selection for that sensor type. However, a given sensor/algorithm system has an absolute limitation based on the physical phenomena that system is capable of sensing.
Therefore, we perform decision-level fusion of the outputs from single-channel algorithms and we choose to combine systems whose information is complementary across operating conditions. That way, the final fused system will be robust to a variety of conditions, which is a critical property of a countermine detection system. In this paper, we present the analysis of fusion algorithms on data from a sensor suite consisting of high frequency radar imagery combined with hyperspectral long-wave infrared sensor imagery. The main type of fusion being considered is Choquet integral fusion. We evaluate performance achieved using the Choquet integral method for sensor fusion versus Boolean and soft "and," "or," mean, or majority voting.
Creating a minefield requires disturbing the soil. This disturbance alters the soil properties and processes in a measurable way. The U.S. Army is investigating techniques to exploit the altered properties of disturbed soil to assist in the detection of buried landmines. The differential quartz reststrahlen signatures between disturbed and undisturbed soil at the long wave infrared (LWIR) region have shown promise in past field tests.(1,3)We have initiated ground-based measurements using a non-imaging spectral sensor to investigate the phenomenology of LWIR disturbed soil signature. Our primary goal is to develop rainfall-dependent models to predict the degradation of the differential reststrahlen signature for varying soil types. A bare soil test site with strong quartz reststrahlen signature was selected for our initial investigation. The disturbed and undisturbed soil spectral signatures at the LWIR regions were obtained after multiple rain events using a Design and Prototypes field portable Fourier transform infrared (FTIR) spectrometer. The intensity and total amount of rainfall were recorded using a high-resolution tipping-bucket rain gauge. In addition to these measurements, photomicrographs of the disturbed soil were obtained after rainfall events, and X-ray diffraction analyses were conducted to obtain detailed soil mineralogy of the test site. We present these results and discuss the changes in the spectral characteristics of disturbed soil as a function of rainfall amount and intensity.
We report an experimental study of the use of DuPont photopolymer Holographic Recording Film to record high resolution reflection holograms of an integrated circuit chip with an initial film-to-object separation of 1.5 millimeters. A two-step H1-H2 recording sequence is used to transfer H1 images to the H2 film plane. Because of emulsion shrinkage, the optimum H2 recording wavelength is approximately 10 nm shorter than the H1 recording wavelength. The H2 reflection holograms, which are incoherently illuminated and viewed through a conventional microscope, reconstruct high resolution images with clearly resolved micron sized features.
Conference Committee Involvement (4)
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV
5 April 2010 | Orlando, Florida, United States
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIV
13 April 2009 | Orlando, Florida, United States
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII
17 March 2008 | Orlando, Florida, United States
Detection and Remediation Technologies for Mines and Minelike Targets XII