Discovery of Geospatial Discriminating Patterns from Remote Sensing Datasets.

Large amounts of remotely sensed data calls for data mining techniques to fully utilize their rich information content. In this paper, we study new means of discovery and summarization of knowledge contained in the spatial patterns of remote sensing datasets. Several geospatial feature variables are fused together, and the vector of their values at each spatial cell is considered as a transaction to be used in association analysis. The concept of emerging patterns is applied to ascertain the variables that exert dominant influence on the distribution of a selected class variable. A new value-iteration method is introduced to optimally split the spatial domain of the selected variable into two classes. This division is used to calculate the set of patterns that are emerging with respect to the two classes; these patterns are the controlling factors—they are responsible for the spatial distribution of the class variable. A method for a concise summarization of controlling factors is ...

• Type: Conference
• Year: 2009
• Where: SDM
• Authors: Wei Ding 0003, Tomasz F. Stepinski, Josue Salazar
• Keywords: Computer Science, Remote Sensing Datasets, SDM 2009, Spatial Frequent Patterns, Spatial Patterns