Rule-Based Classification Systems Using Classification and Regression Tree (CART) Analysis

Abstract

Incorporating ancillary data into image classification can increase classification accuracy and precision. Rule-based classification systems using expert systems or machine learning are a particularly useful means of incorporating ancillary data, but have been difficult to implement. We developed a means for creating a rule-based classification using classification and regression tree analysis (CART), a commonly available statistical method. The CART classification does not require expert knowledge, automatically selects useful spectral and ancillary data from data supplied by the analyst, and can be used with continuous and categorical ancillary data. We demonstrated the use of the CART classification at three increasingly detailed classification levels for a portion of the Greater Yellowstone Ecosystem. Overall accuracies ranged from 96 percent at level 1, to 79 percent at level 2, and 65 percent at level 3.