Land Use Classification of Remote Sensing Image with Gis Databased on Spatial Data Mining Techniques

Data mining techniques are studied to discover knowledge from GIS database and remote sensing image data in order to improve land use classification. Two learning granularities are proposed for inductive learning from spatial data, one is spatial object granularity, the other is pixel granularity. The characteristics and application scope of the two granularities are discussed. We also present an approach to combine inductive learning with conventional image classification methods, which selects class probability of Bayes classification as learning attributes. A land use classification experiment is performed in the Beijing area using SPOT multi-spectral image and GIS data. Rules about spatial distribution patterns and shape features are discovered by C5.0 inductive learning algorithm and then the image is reclassified by deductive reasoning. Comparing with the result produced only by Bayes classification, the overall accuracy increased 11 percent and the accuracy of some classes, such as garden and forest, increased about 30 percent. The results indicate that inductive learning can resolve the problem of spectral confusion to a great extent. Combining Bayes method with inductive learning not only improves classification accuracy greatly, but also extends the classification by subdivide some classes with the discovered knowledge.