Using High Resolution Multispectral and/or Hyperspectral Imagery to Improve Digital Land Cover Classification From Low Resolution Multispectral Imager

Observera proposes to integrate several innovative fusion concepts (resolution enhancement technology, high resolution signature data, and spatial texture) into the Army's process of Land Cover / Land Use (LCLU) classification from LANDSAT. Thisintegration will result in substantially improved classification over current techniques. Specific focus will be on automating as much of these techniques as possible in order to maximize their utility for quick response missions. In addition, we willalso develop the collection strategies necessary for cost-effective collection of the high-resolution imagery needed to support these new concepts. Demonstrating these capabilities lays an effective foundation for the development of an integratedclassification process in Phase II, which will consist of the development of a prototype LCLU system. The Phase II effort will use rapid prototyping methodologies in order to develop a series of incrementally more capable prototype systems.Commercialization of the integrated system is one possible avenue, either as an add-on to a commercial image processing package or bundled with a commercial package as a complete system. Additionally, several of the individual components have independentcommercial viability. In the larger context, the concepts developed here also enhance our ability to provide customized feature extraction capabilities to government and commercial customers worldwide.