Fuzzy ARTMAP driven features selection: towards a system to design information fusion for the geographic discovery

Abstract

The availability of ground-level and overhead images is increasing fast. However, the potentialities of joint usage of these images on information fusion systems for in-deep geographic discovery, by answering the typical binomial quest: what and where are still to be completely evaluated. It is, therefore, necessary to establish a variety of procedures to study these potentialities including on how to assess domain-invariance between this imagery related to ground-level and overhead capture domains for example. We addressed in this paper a novel framework for one prospect for an optimal set of handcrafted domain-invariant features for ground the optimized usage of multiple sources of information on a feature level based fusion system. All of this was done framed by the usage of small datasets and, also, of interpretable classifiers (human-centered computing) and it paves the way towards the proposition of application software to be used potentially as a didactic tool with emphasis on the botanic domain. The results also endorse a domain-invariance that was already implicit on the results of previously published papers. Broadly evaluating, these results connecting image datasets on contrasting scales can launch new investigations in the scope of scale-free phenomena.

Keywords: methodological framework, dimensionality reduction, feature selection, training, interpretability, automatic, tree-bark, land use