Software tools for assisting the multisource imagery analyst

Abstract

Increasingly demanding military requirements and rapid technological advances are producing reconnaissance sensors with greater spatial, spectral and temporal resolution. This, with the benefits to be gained from deploying multiple sensors co-operatively, is resulting in a so-called data deluge, where recording systems, data-links, and exploitation systems struggle to cope with the required imagery throughput. This paper focuses on the exploitation stage and, in particular, the provision of cueing aids for Imagery Analysts (IAs), who need to integrate a variety of sources in order to gain situational awareness. These sources may include multi-source imagery and intelligence feeds, various types of mapping and collateral data, as well the need for the IAs to add their own expertise in military doctrine etc. This integration task is becoming increasingly difficult as the volume and diversity of the input increases.The first stage in many exploitation tasks is that of image registration. It facilitates change detection and many avenues of multi-source exploitation. Progress is reported on the automating this task, on its current performance characteristics, its integration into a potentially operational system, and hence on its expected utility.We also report on the development of an evolutionary architecture, 'ICARUS' in which feature detectors (or cuers) are constructed incrementally using a genetic algorithm that evolves simple sub-structures before combining, and further evolving them, to form more comprehensive and robust detectors. This approach is shown to help overcome the complexity limit that prevents many machine-learning algorithms from scaling up to the real world.

Military Applications of Machine Learning: A Bibliometric Perspective

“…In general terms, it can be said that the data that predominate in the military context are more complex to process than those in a conventional organization. In the study we have carried out, the main sources found are biometric data, e.g., from electroencephalograms [70]; data from military experts and commanders (e.g., field assessments and weaponry needed in that particular field), which should be used in conjunction with the other data to build decision systems [35]; the Internet of things, smart and connected devices widely used by the military, generating large volumes of information over time [71], e.g., those provided by radars [72]; the Internet of battlefield things, which connects soldiers with smart technology in weapons and other objects to give troops "extra sensory powers" [73]; military personnel data, e.g., those obtained in screening interviews [74]; military veterans' data obtained, e.g., from their administrative personal files [75]; and other data, both internal and open in any format (including videos, images and speeches), e.g., those coming from unmanned aerial vehicles [76], videos for facial recognition [77,78], data from the Internet, etc. ; -Insight generation for military.…”

Automated image registration with ARACHNID

“…At Dstl a testbed system (Privett et al, 2003) has been developed to provide computer aided assistance to IAs, aimed in particular at automating some routine aspects of their workload and at directing the analyst's attention towards features of potential interest. The IA can then focus on problems that require their experience and reasoning skills rather than on mundane -but, nonetheless, essential -image handling tasks.…”