Experiments on Remote Sensing image cube and its OLAP

OLAP can answer questions such as 'what next' and 'what if'. OLAP, which always needs the support of data warehouse, is a complement to data mining. In the early steps of data mining progress, OLAP tools might be helpful in tasks such as exploring the data sets, locating more important variables and unwanted data, which can help users to understand the source data and quicken the data mining progress. In order to find data or information quickly in great volumes of Remote Sensing (RS) image data sets, for one thing, metadata bases need to be built; for another thing, research and development are required to deal with spatial OLAP application servers, which can manipulate spatial data warehouse containing RS images. Thematic images such as TM2, TM4, land cover, transportation, slope and the result image of ERDAS IMAGINE Expert Classifier, etc., have been inputted into MS Access tables. On grounds of these tables and the relational multi-dimensional data model, dimension tables and the fact table have been generated, and furthermore a RS image cube structure has been constructed. After the pre-computation and materialization, a RS image cube has been created. Experiments of OLAP on this cube have been carried out. Owning to the pre-computation and materialization, queries on the cube can be carried out with no delay. As the experiments show, if data warehouse and OLAP are adopted, not only different factors and their concept hierarchies can be used conveniently, but also queries speed up.