The Anti-Invasion Mine Signature Measurement and Assessment for Remote Targeting (AIMSMART) program has undertaken a lidar mine signature data collection for ONR to characterize electro-optic (EO) signatures of anti-invasion mines and environmental factors affecting their detection in the littorals. Two lidar sensors, one 3-D and one polarimetric, both developed by Arete, were fielded at the FRF test facility in Duck, NC. Data were collected with these sensors over a wide variety of mine targets, obstacles, backgrounds, water quality, and wave movements. The principle goal of this analysis is to characterize lidar signature features, especially 3-D, of in-water mines and correlate those features to physical processes in the VSW and SZ environments. This paper describes the approach to characterizing these mine signatures and presents initial results from the analysis.
Two separate data collections using Arete Associates' FLASH lidar are presented. The hardware and the experimental arrangements are discussed. An airborne data collection over military targets in clear and obscuring camouflage environments provided high-resolution three-dimensional images for combat identification purposes. In the second field test, the sensor was suspended from a crane above the ocean surface to acquire FLASH imagery of anti-landing mines and obstacles in the highly turbid surf zone environment over a wide range of surf zone conditions.
The objective of the US Army Hyperspectral Mine Detection Phenomenology program was to determine if spectral disciminants exist that are useful for the detection of land mines. A primary goal wa to determine the presence and persistence of spectral features produced by buried anti- tank mines as associated with soil properties and vegetation changes over time. Details of the collections are documented in the ERIM International Technical Report 10012200-15-T, 'Mine Spectral Signature Collections and Data Archive', March 1999. This paper describes the HMDP project and focuses on the initial phase of controlled experimental measurements of spectral mine signatures in ground-based US collections. The foreign data collections are not addressed in this paper. Some of the HMDP project's mine spectral signature result are highlighted here. Detailed analyses of these data were performed and is described in a companion paper in this conference titled '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.
The Coastal Systems Station (CSS) at Panama City, FL is developing an airborne multispectral sensor system which flies on an unmanned aerial vehicle for detecting mines in a coastal environment. This system is called the Coastal Battlefield Reconnaissance and Analysis (COBRA) system and has successfully completed preliminary developmental testing (DT-0). For this program, the Environmental Research Institute of Michigan (ERIM) developed a fieldable ground station including integrated aircraft tracking, real-time sensor data analysis, and a post processor testbed for developing and evaluating mine and minefield detection algorithms. A fully adaptive multispectral Constant False Alarm Rate mine detection algorithm was implemented in the post-processor by ERIM, along with patterned and scatterable minefield detection algorithms developed by CSS. The algorithms do not require prior knowledge of mine spectral signatures and thus are ideal for detecting a wide variety of mines with unknown or changing spectral signatures. COBRA DT-0 testing has been performed on actual minefields deployed at coastal and inland test sites. Preliminary results show that the COBRA system, coupled with these algorithms, meets the required minefield detection performance goals. This paper reviews the algorithm theory and implementation, overviews the ground station design, and presents minefield detection results from actual minefield imagery collected over realistic scenes during DT-0 testing.
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.