Abstract

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 introduced using a similarity measure that is custom-made for the type of patterns stemmed from remote sensing measurements. Using such a similarity measure, controlling factors are clustered providing brief description of different manners, in which the class variable is constrained by the explanatory variables. We evaluate our method in a real-world application pertaining to the density of vegetation within the continental United States. Examination of patterns related to the high vegetation cover provides a summary of data dependencies that helps to develop a better empirical model of the vegetation growth.

Citations