Abstract
The development of explainable artificial intelligence (xAI) methods for scene classification problems has attracted great attention in remote sensing (RS). Most xAI methods and the related evaluation metrics in RS are initially developed for natural images considered in computer vision (CV), and their direct usage in RS may not be suitable. To address this issue, in this paper, we investigate the effectiveness of explanation methods and metrics in the context of RS image scene classification. In detail, we methodologically and experimentally analyze ten explanation metrics spanning five categories (faithfulness, robustness, localization, complexity, randomization), applied to five established feature attribution methods (Occlusion, LIME, GradCAM, LRP, and DeepLIFT) across three RS datasets. Our methodological analysis identifies key limitations in both explanation methods and metrics. The performance of perturbation-based methods, such as Occlusion and LIME, heavily depends on perturbation baselines and spatial characteristics of RS scenes. Gradient-based approaches like GradCAM struggle when multiple labels are present in the same image, while some relevance propagation methods (LRP) can distribute relevance disproportionately relative to the spatial extent of classes. Analogously, we find limitations in evaluation metrics. Faithfulness metrics share the same problems as perturbation-based methods. Localization metrics and complexity metrics are unreliable for classes with a large spatial extent. In contrast, robustness metrics and randomization metrics consistently exhibit greater stability. Our experimental results support these methodological findings. Based on our analysis, we provide guidelines for selecting explanation methods, metrics, and hyperparameters in the context of RS image scene classification.
Abstract (translated)
针对场景分类问题的可解释人工智能（xAI）方法在遥感（RS）领域引起了广泛关注。大多数用于遥感的xAI方法和相关评估指标最初是为计算机视觉（CV）中的自然图像设计的，直接应用于遥感中可能并不合适。为了应对这一挑战，在本文中，我们探讨了在遥感图像场景分类背景下解释方法和度量标准的有效性。具体来说，我们从理论分析和实验两个方面对五种成熟的特征归因方法（遮挡、LIME、GradCAM、LRP 和 DeepLIFT）应用于三种遥感数据集的十项解释指标进行了深入探讨，这些指标涵盖了五个类别（忠实度、鲁棒性、定位、复杂性和随机化）。我们的理论分析揭示了在解释方法和评价指标方面的关键限制。基于扰动的方法（如遮挡和 LIME），其性能严重依赖于扰动基准和遥感场景的空间特性。基于梯度的方法（例如 GradCAM）在存在多个标签时难以处理，而一些相关性传播方法（LRP）可能会不对称地分配类别的重要性相对于空间范围而言。类似地，我们还发现评估指标中也存在着局限性。忠实度度量与基于扰动的方法面临同样的问题；定位度量和复杂性度量对于具有大空间范围的类别不可靠。相比之下，鲁棒性和随机化度量始终表现出更高的稳定性。我们的实验结果支持了这些理论上的发现。 根据分析，我们为遥感图像场景分类中的解释方法、指标和超参数选择提供了指导原则。
URL
https://arxiv.org/abs/2507.05916
