With the French military recently launching its CSO 2 optical reconnaissance satellite, which will provide the highest-resolution Earth observation images ever produced by a European satellite, it reinforces the value of leveraging comprehensive Imagery Intelligence (IMINT) capabilities.
As highlighted in the first post in our IMINT series, Hexagon’s information technologies serve as the foundation behind many of the transformative solutions shaping IMINT.
For our second post, I would like to discuss how data processing is a critical component of IMINT. Coming from the geospatial industry, we believe that decision-makers want access to native data to have unadulterated pixels and information.
This means avoiding the over processing of the data, as well as preventing processed data from being in proprietary formats or within specific geodatabases. Ultimately, this can reduce the audience that needs to access this information.
Before data gets operationalized, it comes in a raw form directly from satellites. Due to atmospheric effects and specifics of the sensors used, this data is rarely usable in its raw form. However, tools like ERDAS IMAGINE offer direct read of satellite data from sources like Sentinel-2 among others.
With its satellite imagery processing capabilities, the solution allows users to transform satellite imagery into common geospatial data, feeding map products and intelligence workflows. It also supports the most-common satellite sensor models, allowing users to quickly setup workflows to control imagery, mosaic it together and optimize it for use. These workflows can be automated and distributed, so data is prepared instantly and with little to no effort – making it rapidly available and ready when needed.
Along with the management of raw satellite imagery, Hexagon also offers photogrammetric solutions that can process imagery to improve overall accuracy, as well as create ortho imagery and highly detailed 3D point clouds. These processes can be automated and batched, which frees the user to perform other tasks through spatial model operators.
Machine Learning and AI processes can also be embedded to further automate tasks such as object and change detection on the imagery, and even the qualification of point cloud data.
Hopefully, I don’t sound too much like an advertisement! I can’t help but be amazed at what is possible with today’s data processing capabilities, especially as sources of data continue to grow exponentially. I am always impressed when I see thousands of images being processed into a coherent tiled map or even point cloud.
Of course, it doesn’t end here. There’s virtually no limit on what can be achieved, especially when it comes to using Machine Learning to detect objects, auto-generate features, and even make full 3D models.
Stay tuned for our upcoming blog posts in this series, which will cover the wonders of data distribution, intelligence extraction, and operational use.
