Techniques and experience in mining remotely sensed satellite data

摘要

The paper presents a set of requirements for a data mining system for mining remotely sensed satellite data based on a number of taxonomies that characterize mining of such data. The first of these taxonomies is based on knowledge of the mining objectives and mining algorithms. The second is based on various relationships that are found in data, including those between different types of data, different spatial locations of the data and different times of data capture. The paper then describes the ADaM data mining system, which was developed to address these requirements. The paper describes several data mining techniques that have been applied to remotely sensed data. The first type is target independent mining, which mines data for transients and trends, with mined results representing a highly concentrated form of the original data. The second type is the mining of vectors (representing multi-spectral or fused data) for association rules representing relationships between the various types of data represented by the elements of the vector. The third type mines data for association rules that characterize the texture of the data.