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May 6th, 2014
Where’s the whammy in WAMI and other motion imagery inputs?

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Wide Area Motion Imagery is a growing data source in the geospatial intelligence arena. There are a number of interesting platforms and programs from the Gorgon Stare that captured several square kilometers to the Autonomous Real-Time Ground Ubiquitous Surveillance (ARGUS) system with coverage of more than 100 square kilometers. The Air Force Distributed Common Ground System (DCGS) now relies primarily on full motion high resolution imagery, and it is this data source and its heavy volume that is escalating  the amount of raw data collected, potentially drowning analysts.

The growth of the demand for motion video for intelligence has been phenomenal, and even exponential. Why such an escalation in this WAMI sensing capability if there are issues with data management and the need for a large crew of analysts? What does this sensor afford that cannot be captured otherwise? Given the recent GeoInt Conference, it seems fitting to explore a unique and escalating sensor set that is slowly making its way into civilian uses.

Magic of Motion

By persistent and precise surveillance, WAMI sensors offer unprecedented situational awareness as they allow for going back in time to understand and query consequences or unfolding situations. With real-time delivery, troops can fix and follow targets over a wide area, gaining insight into larger interactions. Tracing actions and routes allow for understanding origins and patterns.

Still imagery simply doesn’t afford this connectivity to the unfolding situation. Understanding static imagery also requires a trained eye where details don’t present themselves without some training. In contrast, full motion is more intuitive where motion uncovers action and collusion, as well as impact if not motive and means.

Dynamic Data

Watching motion in context is a window that builds understanding more quickly than other means. The unique dynamic nature of WAMI gives rise to informed action in a more connected manner than slow-paced and disconnected intelligence sources. Simply sharing such imagery in the close context of a specific location allows for a rapid comprehension of surroundings , actors, and patterns that can feed informed and effective action.

There’s work ongoing on software to understand and manage this information, such as the Persistent Stare Exploitation and Analysis System (PerSEAS) software system. This software was developed to automatically and interactively discover actionable intelligence from wide area motion imagery (WAMI) of complex urban, suburban and rural environments. The software has been used in a forensic mode, working through days of WAMI data to identify threat activities and the underlying threat indicators. Armed with both real-time WAMI inputs, and the forensic automated time travel capability, it’s no wonder that this input has gained a rapid rise.

New Volumes

The ultimate goal of all imagery sources is quick integration into many systems and to many individuals, with delivery and connectivity to the Web as a growing necessity. The sheer volume of data captured by WAMI sensors makes it difficult to manage, let alone distribute the data. These sensors collect full color motion and even infrared bands that make compression necessary, and even then there are challenges in getting this imagery to data centers and end users, with the need for high bandwidth connectivity.

The high data volumes of WAMI imagery mean that it is a difficult data source to process and exploit. Just as computers experience Moore’s law in terms of the growing amount of memory needed, coupled with the decreasing costs for that memory, sensors seem to have a parallel in terms of the acceleration of data that can be generated. Unique to this intelligence data is also the need to keep it secure. With advancements in telecommunication networks there have been improvements in distribution, but as demand grows more sensors will emerge that will require increasing bandwidth.

Given how far motion imagery has come in terms of adoption and use, it’s hard to reconcile how young this data source is for intelligence operations. With under ten years of use, the ability to view and understand action has taken precedence over more static data sources. While this source has proven its worth, the real whammy in WAMI will be realized when we have systems that can quickly analyze, process, distribute and archive this information for improved safety and efficiency.

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