Quantitative Analysis of Primary Attribution Explainable Artificial Intelligence Methods for Remote Sensing Image Classification
This work addresses the need for better evaluation of XAI techniques in remote sensing, but it is incremental as it focuses on analysis rather than introducing new methods.
The paper tackled the problem of evaluating explainable AI (XAI) methods for remote sensing image classification by comparing them quantitatively across desired properties, resulting in insights and recommendations for selecting appropriate methods to understand model decisions.
We present a comprehensive analysis of quantitatively evaluating explainable artificial intelligence (XAI) techniques for remote sensing image classification. Our approach leverages state-of-the-art machine learning approaches to perform remote sensing image classification across multiple modalities. We investigate the results of the models qualitatively through XAI methods. Additionally, we compare the XAI methods quantitatively through various categories of desired properties. Through our analysis, we offer insights and recommendations for selecting the most appropriate XAI method(s) to gain a deeper understanding of the models' decision-making processes. The code for this work is publicly available.