Learning Classifier Systems for Hyperspectral Images Processing
نویسندگان
چکیده
In this article, two learning classifier system based classification 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. The comparison of the system includes differences in evolution accuracy and parameter refinement.
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