A Framework for Interactive Visual Interpretation of Remote Sensing Data

Abstract

Machine learning methods have shown tremendous success in understanding earth observation data; however, recently, there is a rising claim toward explainable machine learning approaches. Concerned researchers found interpretable visualizations to be greatly helpful in understanding how a model works. In this research, we propose a framework for interactive and interpretable visualization of remote sensing data using two machine learning models and an Elasticsearch (ES) database. Two explainable machine learning models, namely, bag-of-visual-words (BoVWs) and latent Dirichlet allocation (LDA) are chosen to model the data in an unsupervised manner and give a textual representation. The textualized remote sensing data are stored in an ES database. This framework offers several fast content-based search functionalities exploiting the full-text query capabilities of ES based on the respective representations and also offers an efficient storage mechanism for the data.