Feature-free Explainable Data Mining in SAR Images Using Latent Dirichlet Allocation

Abstract

In this paper, we propose a promising approach for the application-oriented content classification of space-borne radar imagery that presents an interesting alternative to popular current machine learning algorithms. In the following, we consider the problem of unsupervised feature-free satellite image classification as an explainable data mining problem for regions with no prior information. Three important issues are addressed here: explainability, unsupervision and feature-independence. There is an increasing demand towards explainable machine learning models as they strive to meet the “right to explanation”. The importance of feature-free classification stems from the problem that different classification outcomes are obtained from using different features and the complexity of computing sophisticated image primitive features. Developing unsupervised discovery techniques helps overcome the limitations in object discovery due to the lack of labelled data and the dependence on features. In this paper, we demonstrate the applicability of the Latent Dirichlet Allocation (LDA) model, one of the most established unsupervised probabilistic methods, in discovering the latent structure of synthetic aperture radar (SAR) data. The idea is to use LDA as an explainable data mining tool to discover scientifically explainable semantic relations. The suitability of the approach as an explainable model is discussed and interpretable topic representation maps are produced which practically demonstrate the idea of “interpretability” in the explainable machine learning paradigm. LDA discovers the latent structure in the data as a set of topics. We create the interpretable visualizations of the data utilizing these topics and compute the topic distributions for each land-cover class.