Anti-tank (AT) landmines slow down and endanger military advances and present sizeable humanitarian problems. The remediation of these mines by direct human intervention is both dangerous and costly. The Intelligent Systems & Robotics Group (ISRG) at New Mexico Tech has provided a partial solution to this problem by developing an Unmanned Ground Vehicle (UGV) to remediate these mines without endangering human lives. This paper presents an overview of the design and operation of this UGV. Current results and future work are also described herein.To initiate the remediation process the UGV is given the GPS coordinates of previously detected landmines. Once the UGV autonomously navigates to an acceptable proximity of the landmine, a remote operator acquires control over a wireless network link using a joystick on a base station. Utilizing two cameras mounted on the UGV, the operator is able to accurately position the UGV directly over the landmine. The UGV houses a self-contained drill system equipped with its own processing resources, sensors, and actuators. The drill system deploys a neutralizing device over the landmine to neutralize it. One such device, developed by Science Applications International Corporation (SAIC®), employs incendiary materials to melt through the container of the landmine and slowly burn the explosive material, thereby safely and remotely disabling the landmine.

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Multi-pass encoding is a technique employed in the field of video compression that maximizes the quality of an encoded video sequence within the constraints of a specified bit rate. This paper presents research where multi-pass encoding is extended to the field of hyperspectral image compression. Unlike video, which is primarily intended to be viewed by a human observer, hyperspectral imagery is processed by computational algorithms that generally attempt to classify the pixel spectra within the imagery. As such, these algorithms are more sensitive to distortion in the spectral dimension of the image than they are to perceptual distortion in the spatial dimension. The compression algorithm developed for this research, which uses the Karhunen-Loeve transform for spectral decorrelation followed by a modified H.264/Advanced Video Coding (AVC) encoder, maintains a user-specified spectral quality level while maximizing the compression ratio throughout the encoding process. The compression performance may be considered near-lossless in certain scenarios. For qualitative purposes, this paper presents the performance of the compression algorithm for several Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Hyperion datasets using spectral angle as the spectral quality assessment function. Specifically, the compression performance is illustrated in the form of rate-distortion curves that plot spectral angle versus bits per pixel per band (bpppb).

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