Bidirectional Flow Decision Tree for Reliable Remote Sensing Image Scene Classification

Abstract

Remote sensing image scene classification (RSISC), which aims to classify scene categories for remote sensing imagery, has broad applications in various fields. Recent deep learning (DL) successes have led to a new wave of RSISC applications; however, they lack explainability and trustworthiness. Here, we propose a bidirectional flow decision tree (BFDT) module to create a reliable RS scene classification framework. Our algorithm combines BFDT and Convolutional Neural Networks (CNNs) to make the decision process easily interpretable. First, we extract multilevel feature information from the pretrained CNN model, which provides the basis for constructing the subsequent hierarchical structure. Then the model uses the discriminative nature of scene features at different levels to gradually refine similar subsets and learn the interclass hierarchy. Meanwhile, the last fully connected layer embeds decision rules for the decision tree from the bottom up. Finally, the cascading softmax loss is used to train and learn the depth features based on the hierarchical structure formed by the tree structure that contains rich remote sensing information. We also discovered that superclass results can be obtained well for unseen classes due to its unique tree structure hierarchical property, which results in our model having a good generalization effect. The experimental results align with theoretical predictions using three popular datasets. Our proposed framework provides explainable results, leading to correctable and trustworthy approaches.

Keywords

• explainable artificial intelligence (XAI)
• scene classification
• decision tree
• cascaded softmax
• remote sensing big data