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Susan Smith, Managing Editor
Rapid Image Access Technology Without Compression
by Susan Smith
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Pixia Corp. announced at ESRI that they had been selected by the Department of Homeland Security (DHS) to provide a baseline capability for serving geospatial imagery via the Department's Integrated Common Analytical Viewer (iCAV) system. According to the press release, “iCAV is a web-based geospatial analytical and situational awareness system that helps the DHS mission partners to better prepare, prevent, respond and recover from natural and man-made disasters.” Pixia is a provider of rapid image access technology that allows the rapid ingestion and dissemination of large amounts of satellite and aerial imagery, which is what DHS wanted.
“Pixia's software allows for high performance image access via Open Geospatial Consortium(R) (OGC) standard web services, such as a Web Map Service (WMS) and a Web Coverage Service (WCS). This provides a fully interoperable framework that easily integrates into an Enterprise Service Oriented Architecture (SOA).”
Our technology is a raster file structure,” said Patrick Ernst, director, Business Development. “We’re all about increasing performance and scalability when it comes to storing and retrieving data, particularly image data and pixels although it does apply to other data. It’s not a compression format, it’s more of a file system within a file.”
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A lot of Pixia’s customers are in the defense, intelligence and homeland security markets, where compression is not always the ideal way to handle data. Organizations often compress at very high compression ratios because that’s the only way it becomes easily manageable, said Ernst. Pixia lets you store that uncompressed data and as in the example of DHS, you could take the entire continental U.S. and make one file out of it.
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Compared with compression, Ernst said Pixia is “extremely fast, we’re talking roughly 100 milliseconds for accessing anywhere randomly within a gigantic image. You could be talking hundreds of thousands of pixels by hundreds of thousands of pixels and you’ll access random. We have pyramid levels in there, and you can reduce resolution data sets to whatever level you want. So you’re not constrained to base powers of 2. If for some reason you want a 6-inch pyramid level if you then want a 8-inch or 9-inch or a 1.2 meter you can set it at whatever you want.”
If you have a data set of IKONOS imagery, that’s one meter per pixel ground sample distance, and see that its native resolution is the level zero, Pixia as well as Mr. SID, ECW, IMG, even National Imagery Transmission Format (NITF), allow you to create pure mid levels of the imagery. This is so that when a user wants to look at a larger area but they don’t want to zoom in all the way, that scaling has already been done for you. The way the other formats allow you to do this is by taking every other pixel and dropping out the middle pixel. Pixia lets you set those pyramid levels at whatever you want which works well for sensor fusion capabilities. This is good when you have different sensors at different resolutions and you want to actually find common ground there, as in fusing together a DigitalGlobe QuickBird image with a 1 meter IKONOS image to create a 1 meter dataset.
“What we have in some ways a geo raster database within a flat file. What that allows us to do is to stack these different images on top of each other. You can update them quite quickly. So you’re looking at over 40 terabytes of imagery that’s being served up by this one server,” explained Ernst.
The resolution of the data used is really a function of the sensor and the data constraint, Ernst stated. “We can super sample it and zoom in past the resolution of the data. We can handle any resolution data you have, no limitation. Because we’re actually overlaying different resolutions of data, it’s not like we’re saying here’s the base resolution for the whole world and let’s plop that in there and now you’re constrained to that. You can just pull in data at different resolutions.”
For the DHS iCAV program, Pixia will be doing one meter of the whole country with 133 cities sprinkled inside the one meter. There will be 15 meter of the whole world, at different resolutions. The 133 cities, based on critical infrastructure protection, will be much higher resolution varying from 6-inch to 1-foot. The 1-meter will be much coarser resolution yet it gives you a broad, synoptic coverage of the entire continent of the U.S.
“The reason we’re able to ingest much more rapidly than your average spatial database, or spatial and relational database stack, which are the more traditional approach for ingesting big amounts of imagery,” Ernst explained, “is you have a limitation of 2-5 gigabytes per hour to bring that data in. That’s a hardcore limitation because you’re actually building up that database. When you take a flat file approach, we’re seeing an excess of 50 gigabytes an hour per processor. So if you’re looking at serving imagery across an enterprise and you have quite a few processors available to you, you’re suddenly looking at exponential increases on ingest performance.”