Genetic Programming for Classification of Remote Sensing Data

Abstract

The overall objective of this research was to develop an adaptive machine learning technique for the classification of remote sensing data. The genetic programming paradigm was implemented to classi1 vegetation, bare soil, and burnt-over areas using SPOT multispectral data. Two SPOT imageries obtained on 31 Dec. 1986 and 15 Jan. 1988 were used in this study. The results show that the genetic programming paradigm was very effective in classi1’ing the data set (e.g., the best classification accuracy obtained was 99% for the training samples). Moreover, the computer programs derived from genetic programming allowed important variables for classification to be identified. Classification results for the machine learning approach were then compared to the results obtained using a conventional statistical approach (i.e., the Gaussian maximum likelihood classifier). The comparison shows that the classification results for both approaches are similar.