Deep learning detection of types of water-bodies using optical variables and ensembling

Abstract

Water features are one of the most crucial environmental elements for strengthening climate-change adaptation. Remote sensing (RS) technologies driven by artificial intelligence (AI) have emerged as one of the most sought-after approaches for automating water information extraction and indeed. In this paper, a stacked ensemble model approach is proposed on AquaSat dataset (more than 500,000 images collection via satellite and Google Earth Engine). A one-way Analysis of variance (ANOVA) test and the Kruskal Wallis test are conducted for various optical-based variables at 99% significance level to understand how these vary for different water bodies. An oversampling is done on the training data using Synthetic Minority Oversampling Technique (SMOTE) to solve the problem of class imbalance while the model is tested on an imbalanced data, replicating the real-life situation. To enhance state-of-the-art, the pros of standalone machine learning classifiers and neural networks have been utilized. The stacked model obtained 100% accuracy on the testing data when using the decision tree classifier as the meta model. This study has been cross validated five-fold and will help researchers working in in-situ water bodies detection with the use of stacked model classification.

Keywords

• Stacked modeling
• ANOVA
• Smote
• Classification
• Meta learning