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The Civil Air Patrol (CAP) is procuring Airborne Real-time Cueing Hyperspectral Enhanced Reconnaissance (ARCHER) systems to increase their search-and-rescue mission capability. These systems are being installed on a fleet of Gippsland GA-8 aircraft, and will position CAP to gain realworld mission experience with the application of hyperspectral sensor and processing technology to search and rescue. The ARCHER system design, data processing, and operational concept leverage several years of investment in hyperspectral technology research and airborne system demonstration programs by the Naval Research Laboratory (NRL) and Air Force Research Laboratory (AFRL).Each ARCHER system consists of a NovaSol-designed, pushbroom, visible/near-infrared (VNIR) hyperspectral imaging (HSI) sensor, a co-boresighted visible panchromatic high-resolution imaging (HRI) sensor, and a CMIGITS-III GPS/INS unit in an integrated sensor assembly mounted inside the GA-8 cabin. ARCHER incorporates an on-board data processing system developed by Space Computer Corporation (SCC) to perform numerous real-time processing functions including data acquisition and recording, raw data correction, target detection, cueing and chipping, precision image geo-registration, and display and dissemination of image products and target cue information. A ground processing station is provided for post-flight data playback and analysis. This paper describes the requirements and architecture of the ARCHER system, including design, components, software, interfaces, and displays. Key sensor performance characteristics and real-time data processing features are discussed in detail. The use of the system for detecting and geo-locating ground targets in real-time is demonstrated using test data collected in Southern California in the fall of 2004.

Software development is a complex endeavor that encompasses application and implementation layers with functional (refers to what is done) and non-functional (how is done) aspects. The efforts to scale agile software development practices are not wholly able to address issues such as integrity, which is a crucial non-functional aspect of the software development process. However, if we consider most software failures are Byzantine failures (i.e., where components may fail and there is imperfect information on which a component has failed.) that might impair the operation but do not completely disable the production line. In this paper, we assume software practitioners who cause defects as Byzantine participants and claim that most software failures can be mitigated by viewing software development as the Byzantine Generals Problem. Consequently, we propose a test-driven incentive mechanism based on a blockchain concept to orchestrate the software development process where production is controlled by a similar infrastructure based on the working principles of blockchain. We discuss the model that integrates blockchain with the software development process, and provide some recommendations for future work to address the issues while orchestrating software production.

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Operational reconnaissance technical organizations are burdened by greatly increasing workloads due to expanding capabilities for collection and delivery of large volume near-real-time multisensor/multispectral softcopy imagery. Related to the tasking of reconnaissance platforms to provide the imagery are more stringent timelines for exploiting the imagery in response to the rapidly changing threat environment being monitored. The development of a semi-automated softcopy multisensor image exploitation capability is a critical step towards integrating existing advanced image processing techniques in conjunction with appropriate intelligence and cartographic data for next generation image exploitation systems. This paper discusses the results of a recent effort to develop computerassisted aids for the image analyst (IA) in order to rapidly and accurately exploit multispectral/multisensor imagery in combination with intelligence support data and cartographic information for the purpose of target detection and identification. A key challenge of the effort was to design and implement an effective human-computer interface that would satisfy any generic IA task and readily accomodate the needs of a broad range of lAs. .Purpose of the laboratory prototype system SAMME is a laboratoiy prototype system that provides advanced techniques to assist the IA to more rapidly and reliably detect, classify, and identify military targets of interest from temporally-coincident multisensor (e.g., SAR, IR, visible band, multispectral, etc.) imagery. Conventional image processing capabilities are combined with state-of-the-art algorithms for automatic target detection, cartographic area limitation, retrieval and graphical presentation of intelligence information overlays with modern image display technology. The primary contextual information used by SAMME includes: finished intelligence (e.g., military installations, military units, and equipment capabilities); terrain/cartographic data (terrain slope, lines of communication, vegetation, and soils), and imagery-related data (collection parameters, weather conditions, and geodetic control). The contextual information is utilized to cue the IA to probable target locations as an aid for the interpretation of target-related events. These features of SAMME are integrated within a modern graphical user interface that emphasizes user-centered control of the exploitation aids by the IA as opposed to enforcing a rigid regime of image exploitation procedures.To effectively demonstrate and evaluate the utility of SAMME's interactive techniques, an operational environment has been developed to support end-to-end imagery exploitation which includes support for: task and mission requirements, P1 keys, shoebox archive, geopositioning and mensuration, image annotation, and report generation. The SAMME laboratory prototype serves as a technology transfer vehicle for demonstrating and promoting the potential effectiveness of computer aids for image analysts from a broad spectrum of operational imagery e...

Presented in this paper is a methodology that has been used in development of a model-based computer vision system. The methodology focuses on problem solving while using commercial off-the-shelf computer vision software.