Analysis and evaluation of learning classifier systems applied to hyperspectral image classification

Abstract

In this article, two learning classifier systems based on evolutionary techniques are described to classify remote sensing images. Usually, these images contain voluminous, complex, and sometimes erroneous and noisy data. The first approach implements ICU, an evolutionary rule discovery system, generating simple and robust rules. The second approach applies the real-valued accuracy-based classification system XCSR. The two algorithms are detailed and validated on hyperspectral data.