This paper describes the visual, spatial and thermal characteristics, and analysis of dynamic landscape conditions critical to mine detection sensors. The characterization data will be used to develop a geospatial, all-season high fidelity data set to support the modeling of synthetic battlefield environments. Surface and subsurface targets of various materials and sizes were added to natural backgrounds to measure the spectral and thermal changes due to different environmental conditions. The imagery was collected with a four-camera system, each representing the visible near infrared (VNIR), 0.4-1.0 micron spectrum, the near infrared (NIR), 0.9 to 1.7 micron spectrum, the mid-wave infrared (MWIR), 3 to 5 micron spectrum, and the long-wave (LWIR), 8 to 14 micron spectrum. The four imaging systems are mounted on a rotating boom that is raised to approximately 12- meters above ground level to match the nadir aspect airborne imaging systems. Multiple areas within the rotational footprint are selected and measured every 10-minutes through a diurnal cycle. Concurrent meteorological measurements are recorded to identify wind speed and direction, air temperature, surface conditions and relative humidity profiles. The background and target analysis procedure is a process of several steps. First, the regions of interest (ROI's) are selected that identify the target or area to be characterized. Second, the area and statistical values will be calculated for each region of interest. Third, the ROI values are compared to the onsite meteorological station.
Thermal infrared target detection and tracking has challenging and useful applications outside of military scenarios. A digital image processing technique is described for the detection and tracking of free flying bats. Uncalibrated video-rate thermal imagery from a stationary FPA micro-bolometric IR imager is captured on 8-bit digital media. Sequential frames are differenced to remove stationary clutter, and thresholded to select pixels outside of the central distribution of differenced pixel values (both positive and negative). Moving objects then appear as pairs of pixel clusters of differing contrast polarity. For the typical case of a warm bat against a cool background, a pixel cluster exceeding the positive threshold indicates a target location in the current frame and corresponding pixel cluster below the negative threshold indicates the target’s location in the previous frame. These location pairs define a motion vector that is updated every frame. Using the updated motion vector, the next position of the bat is predicted. If a similar-sized pixel cluster of the correct polarity is found at this predicted location, within a selectable error tolerance, then a track is established. This process is iterated frame-by-frame generating an output file of individual bat tracks. This process is described in detail and data are presented from an imaging survey of a bat emergence containing several thousand bats.