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.

翻译：我们提出了一项全面分析，旨在定量评估用于遥感图像分类的可解释人工智能（XAI）技术。我们的方法利用最先进的机器学习方法，在多种模态下执行遥感图像分类。我们通过XAI方法对模型的结果进行定性研究。此外，我们通过多种期望属性类别对XAI方法进行定量比较。通过分析，我们为选择最适合的XAI方法提供了见解与建议，以更深入地理解模型的决策过程。本研究的代码已公开提供。