提升自动驾驶语义分割中光谱信息的可解释性与可靠性
Reliable Explainability of Deep Learning Spatial-Spectral Classifiers for Improved Semantic Segmentation in Autonomous Driving
- 基于激活值与权重分析光谱-空间特征对模型输出的贡献
- 实验证明高光谱数据在语义分割上显著优于三通道与单通道模型
- 提出归一化策略增强模型在真实驾驶场景下的鲁棒性
将高光谱图像（HSI）与深度神经网络（DNN）结合，可通过融合光谱与空间信息提升智能视觉系统精度，适用于自动驾驶中的语义分割任务。为推动此类关键安全系统的研究，亟需精确评估光谱信息对复杂DNN输出的贡献。现有显著性方法如类激活图（CAM）虽被广泛使用，但其可靠性近年受到质疑。本文针对此问题，提出新方法：利用相关DNN层的激活值与权重数据，更准确地捕捉输入特征与预测结果之间的关系。研究旨在评估HSI相比三通道与单通道DNN在性能上的优势，并探讨光谱特征归一化对提升DNN在真实驾驶条件下鲁棒性的影响。
原文摘要 · Abstract (English)
Integrating hyperspectral imagery (HSI) with deep neural networks (DNNs) can strengthen the accuracy of intelligent vision systems by combining spectral and spatial information, which is useful for tasks like semantic segmentation in autonomous driving. To advance research in such safety-critical systems, determining the precise contribution of spectral information to complex DNNs' output is needed. To address this, several saliency methods, such as class activation maps (CAM), have been proposed primarily for image classification. However, recent studies have raised concerns regarding their reliability. In this paper, we address their limitations and propose an alternative approach by leveraging the data provided by activations and weights from relevant DNN layers to better capture the relationship between input features and predictions. The study aims to assess the superior performance of HSI compared to 3-channel and single-channel DNNs. We also address the influence of spectral signature normalization for enhancing DNN robustness in real-world driving conditions.
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