The RISTA II sensor was integrated into the Altus Unmanned Aerial Vehicle (UAV) and flown over Camp Roberts and Ft. Hunter Ligget, CA in July 1998. The RISTA II demonstration system consisted of a long-wave IR imager, a digital data link, and a ground processing facility (GPF) containing an aided target recognizer, data storage devices, and operator workstations. Imagery was compressed on the UAV and sent on the GPF over a 10.71 Mbit per second digital data link. Selected image frames from the GPF were sent near real-time over a T1 link to observers in Rosslyn, VA. The sensor operated in a variety of scanning and framing modes. Both manual and automatic sensor pointing were demonstrated. Seven flights were performed at altitudes up to 7500m and range sup to 60 km from the GPF. Applicability to numerous military and civilian scenarios was demonstrated.
The error resilient entropy coding of the Cambridge University group adapts variable-length blocks of data into fixed-length slots with known positions, in order to protect against possible channel errors during transmission of the discrete wavelet transform (DWT) coefficients. These coefficients have ben compressed based on the standard compression scheme using scalar quantization, runlength and Huffman coding. The DWT bi-orthogonal wavelet is used. The code is illustrated with relatively good performance for FLIR images, compressed at up to 0.09 bits per pixel, and bit error rates at about 10-3. No additional attempt is made to perform decompression restoration.
Battlefield reconnaissance through tactical surveillance video systems requires transmission of images through a limited bandwidth and capacity to achieve aided target recognition (ATR), of which some lossy compression is indispensable. Based on available resolution, ATR can have three functionality goals: (1) detection of a target, (2) recognition of target classes, and (3) identification of individual target membership. Thus, it is desirable to build an intelligent lookup table which maps a specific ATR goal into an appropriate image compression. Such a table may be built implicitly be employing the exemplar training procedure of artificial neutral networks. In order to illustrate this concept, we will introduce a computational metric called feature persistence measure, useful for x-ray luggage inspections, and further generalized here to capture human performance in a tactical imaging scenario.
The CECOM Center for Night Vision and Electro-Optics (C2NVEO) is pursuing a broad based effort to develop Automatic Target Recognizers for a variety of tactical Army applications. The effort includes the development of improved thermal imaging sensors that have fewer artifacts and better sensitivity, uniformity and dynamic range than currently deployed infrared imaging systems. These imagers, along with other sensors, are being used to collect field data of military vehicles and their environment. This digital imagery is being added to an expanding data base that also contains hybrid and synthetic sensor data providing a controlled variability unattainable with real imagery alone. The real imagery provides the validation of this characterized sensor data base. Sensor data from non-imaging sensors is being added to encompass multisensor applications. A facility has been established for training, and testing ATR's where this data can be used in conjunction with a physical terrain board and a sensor test station. The facility has demonstrated the capability to rapidly assess the performance of several ATR's. The current ATR's being investigated are instrumented for rapid, detailed analysis of the algorithms' functions. Full programmability allows investigation of competing algorithms without designing new circuitry. Additional algorithm improvements are being investigated. Techniques using neural nets and optical processing are being pursued. Assembly of "submicron" components using miniaturized packaging concepts is leading to demonstrations of the feasibility of ATR's within stringent platform constraints.