The current extent of publicly available space-based imagery and data products is unprecedented. Data from research
missions and operational environmental programs provide a wealth of information to global users, and in many cases,
the data are accessible in near real-time. The availability of such data provides a unique opportunity to investigate how
information can be cascaded through multiple spatial, spectral, radiometric, and temporal scales. A hierarchical image
classification approach is developed using multispectral data sources to rapidly produce large area landuse identification
and change detection products. The approach derives training pixels from a coarser resolution classification product to
autonomously develop a classification map at improved resolution. The methodology also accommodates parallel
processing to facilitate analysis of large amounts of data.
Previous work successfully demonstrated this approach using a global MODIS 500 m landuse product to construct a
30 m Landsat-based classification map. This effort extends the previous approach to high resolution U.S. commercial
satellite imagery. An initial validation study is performed to document the performance of the algorithm and identify
limitations in the process. Results indicate this approach is scalable and has broad applications to target and anomaly
detection applications. In addition, discussion is focused on how information is preserved throughout the processing
chain, as well as situations where the data integrity could break down. This work is part of a larger effort to deduce
practical, innovative, and alternative ways to leverage and exploit the extensive low-resolution global data archives to
address relevant civil, environmental, and defense objectives.