Mining patterns of change in remote sensing image databases

Remote sensing image databases are the fastest growing archives of spatial information. However, we still have a limited capacity for extracting information from large remote sensing image databases. There are currently very few techniques for image data mining and information extraction in large image data sets, and thus we are failing to exploit our large remote sensing data archives. This paper proposes a methodology to provide guidance for mining remote sensing image databases. The basic idea is to use domain concepts to build generic description of patterns in remote sensing images, and then use structural approaches to identify such patterns in images. We illustrate our proposal with a case study for detecting land use patterns in Amazonia from INPE's remote sensing image database.