Geomorphometric feature selection based on intrinsic dimension estimation
The use of geomorphometric variables or, from a machine learning perspective, geomorphometric features, sometimes coupled with other remote sensing derived variables, is often adopted for the spatial prediction of geoenvironmental properties of interest (e.g. soil and geo engineering mapping) In other circumstances, geomorphometric features are analyzed for unsupervised approaches in the context of landscape classification and pattern recognition. The detection of the relevant features and the distinction between redundant and irrelevant features is crucial both for improving prediction accuracy as well as for reducing computational cost. Moreover, the detection of relevant features improves the interpretation of studied processes. In this short paper, the potentialities of a new feature selection algorithm are evaluated in a supervised learning problem, tested on ad hoc designed synthetic dataset. The feature selection algorithm adopted is a Sequential Forward Selection filter, based on a fractal measure of Intrinsic Dimension, relying on a generalization of the Morisita index. The synthetic data set, built from real topography, is characterized by challenging characteristics as for example a strong linear correlation between relevant features. The tests performed on the data set show that the algorithm correctly individuates the relevant features and the irrelevant ones. Moreover, the impact of subsampling on the feature selection algorithm has been tested, showing a stable response up to roughly the 10% of the original data set. The results of this preliminary study suggest that the algorithm is promising in the geomorphometric context and that it is worth to investigate further its applicability in geomorphometry.