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

Observera's objective for Phase II is to build upon the capabilities developed in Phase I and increase the accuracy and quality of LCLU classification results obtainable from LANDSAT through the innovative application of three techniques (StatisticalPrediction of Bands, Spatial Spectral Bootstrapping and Texture Based Classification). Secondary objectives include increasing the efficiency of the overall workflow, providing compatibility with existing Army systems, and ensuring universal applicabilityof the process. Our Phase II effort will be directed toward building and testing this prototype system, called the Application for Land-use, Land-class Extraction for Geospatial Region Operations (ALLEGRO). The ALLEGRO prototype will be a user-friendly,semi-automated tool that will be compatible with existing Army terrain systems and will support the requirements of operational warfighters. ALLEGRO will address the primary factors limiting classification performance: mixed pixels, similar spectra, datavariance, and training set errors. ALLEGRO will consist of a software application that performs the following functions:-Ingests LRMSI and HRMSI-Assists the operator in selecting optimal patches of HRMSI-Increases the resolution of the MSI using time-coincident panchromatic data-Performs semi-automated land-cover classification-Generates raster class-maps in ERDAS Imagine-compatible format.Commercialization of the integrated system is a primary avenue, either as an add-on to a commercial image processing package (such as ERDAS Imagine) or bundled with a commercial package as a complete system. Additionally, several of the individualcomponents have independent commercial viability that will augment Observera's existing commercial product lines.