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.

本研究针对自动驾驶中语义分割的准确性问题，通过分析光谱信息在复杂深度神经网络输出中的作用，提出了一种新方法，以改进当前的显著性方法。研究发现，利用与深度神经网络相关的激活和权重数据，可以更好地捕捉输入特征与预测之间的关系，从而提升系统在真实驾驶条件下的鲁棒性。

深度学习空间光谱分类器的可靠可解释性以改善自动驾驶中的语义分割