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One problem found when working with satellite images is the radiometricvariations across the image and different images. Intending to improve remotesensing models for the classification of burnt areas, we set two objectives.The first is to understand the relationship between feature spaces and thepredictive ability of the models, allowing us to explain the differencesbetween learning and generalization when training and testing in differentdatasets. We find that training on datasets built from more than one imageprovides models that generalize better. These results are explained byvisualizing the dispersion of values on the feature space. The second objectiveis to evolve hyper-features that improve the performance of differentclassifiers on a variety of test sets. We find the hyper-features to bebeneficial, and obtain the best models with XGBoost, even if the hyper-featuresare optimized for a different method.

Improving the Detection of Burnt Areas in Remote Sensing using Hyper-features Evolved by M3GP