U2: Oral Presentation C Friday, 11:00 – 11:30 am C Gooseberry Falls Room
Statistical,
Spectral and Spatial Knowledge Based Classification System for Multi-Spectral
Remote Sensing Imagery
Ranga Raju Vatsavai, Thomas E. Burk, Paul V.
Bolstad, Marvin Bauer, Tim Mack, Jamie Smedso
University of Minnesota
Department of Forest Resources
Remote Sensing and Geospatial Analysis Laboratory
115 Green Hall
1530 Cleveland Avenue North
St. Paul, MN 55108
vrraju@gis.umn.edu
Thematic classification of multi-spectral
remotely sensed imagery for large geographic regions requires complex algorithms
and feature selection techniques. Traditional statistical classifiers rely
exclusively on spectral characteristics, but thematic classes are often
spectrally overlapping. The spectral response distributions of classes are
dependent on many factors including terrain, slope, aspect, soil type, and
atmospheric conditions. It is now possible to improve traditional classifiers
and develop new classification systems that can incorporate the spatial
knowledge derived from newly available geo-spatial databases. However, it is not
easy to incorporate this additional knowledge into traditional statistical
classification methods. On the other hand, knowledge-based and neural network
classifiers can readily incorporate these spatial databases, but these systems
are often complex to train and their accuracy is only slightly better than
statistical classifiers. In this paper we present a new classification approach,
combining knowledge-based systems and statistical classifiers to minimize the
disadvantages of the traditional approaches. The remote sensing image is first
stratified into spectrally homogeneous regions using the spatial knowledge
derived from ancillary geo-spatial databases. A semi-automated supervised
training is carried out for each of these regions. These regions are then
classified using maximum likelihood classifier. Even in a complex geographic
setting, this classification fusion approach has yielded an overall
classification accuracy of about 90%.
Keywords: Statistical and Knowledge Based Classifiers, Spectral and Spatial Knowledge, Classification Fusion, Thematic Classification