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Many space and airborne sensors can provide imagery suitable for geographical intelligence (GEOINT). The volume of this data continues to grow, while analysts struggle with the volume, variety, and velocity of space-based data. A new intelligence project will demonstrate that GEOINT gleaned through data fusion is greater than the simple sum of GEOINT gleaned from several electro-optical sensor images analyzed in the absence of other imagery.
U.S. intelligence experts are asking industry to blend data from satellite-based multispectral imaging sensors and visible-light sensors to detect heavy building projects and highway construction from space. The aim is to develop tools and techniques for automated broad-area search to detect, monitor, and characterize the progression of natural or man-made events or activities using time-series spectral imagery from several space-based or airborne sensors. Examples include heavy construction; urban development; crop disease propagation; forest fires, flooding and mudslides; human migration, earthquakes, etc.
Officials of the U.S. Intelligence Advanced Projects Agency (IARPA) has released a broad-agency announcement for the Space-based Machine Automated Recognition Technique (SMART) project. The project has two technical areas: data fusion, and algorithms to detect and characterize natural and man-made events.
SMART will rely on geographical information from satellite cameras, and develop multi–spectral and multi–temporal sensor processing to overlay data from infrared and multispectral sensors to make the intelligence analyst’s job easier.
According to miliaryaerospace.com, the project aims at reducing uncertainties inherent in single-sensor data, and reducing the sheer amount of intelligence imagery data that can overwhelm intelligence analysts by developing tools to help analysts analyze intelligence imagery using Big Data.
While one sensor may have resolution sufficient to detect changes and man-made disturbances, intelligence experts still struggle with the inability to analyze images over time because of infrequent satellite orbits or weather cover.
By blending data from several different electro-optical sensors, IARPA experts want to improve the ability to detect and monitor man-made disturbances.
Applications range from geospatial intelligence, disaster recovery, and humanitarian aid, to automated assessment of land-use for commercial purposes.