Hyperspectral Imaging (HSI) is known for its advantages over traditional RGB imaging in remote sensing, agriculture, and medicine. Recently, it has gained attention for enhancing Advanced Driving Assistance Systems (ADAS) perception. Several HSI datasets such as HyKo, HSI-Drive, HSI-Road, and Hyperspectral City have been made available. However, a comprehensive evaluation of semantic segmentation models (SSM) using these datasets is lacking. To address this gap, we evaluated the available annotated HSI datasets on four deep learning-based baseline SSMs: DeepLab v3+, HRNet, PSPNet, and U-Net, along with its two variants: Coordinate Attention (UNet-CA) and Convolutional Block-Attention Module (UNet-CBAM). The original model architectures were adapted to handle the varying spatial and spectral dimensions of the datasets. These baseline SSMs were trained using a class-weighted loss function for individual HSI datasets and evaluated using mean-based metrics such as intersection over union (IoU), recall, precision, F1 score, specificity, and accuracy. Our results indicate that UNet-CBAM, which extracts channel-wise features, outperforms other SSMs and shows potential to leverage spectral information for enhanced semantic segmentation. This study establishes a baseline SSM benchmark on available annotated datasets for future evaluation of HSI-based ADAS perception. However, limitations of current HSI datasets, such as limited dataset size, high class imbalance, and lack of fine-grained annotations, remain significant constraints for developing robust SSMs for ADAS applications.

翻译：高光谱成像（HSI）在遥感、农业和医学领域相较于传统RGB成像具有公认的优势。近年来，其在增强高级驾驶辅助系统（ADAS）感知能力方面受到关注。目前已公开多个HSI数据集，如HyKo、HSI-Drive、HSI-Road和Hyperspectral City。然而，利用这些数据集对语义分割模型（SSM）进行全面评估的研究尚属空白。为填补这一空白，我们在四个基于深度学习的基准SSM上评估了现有带标注的HSI数据集：DeepLab v3+、HRNet、PSPNet和U-Net及其两种变体：坐标注意力机制（UNet-CA）与卷积块注意力模块（UNet-CBAM）。原始模型架构经过调整以适应数据集不同的空间和光谱维度。这些基准SSM使用针对各HSI数据集的类别加权损失函数进行训练，并采用基于均值的评估指标进行评测，包括交并比（IoU）、召回率、精确率、F1分数、特异性和准确率。实验结果表明，能够提取通道级特征的UNet-CBAM模型性能优于其他SSM，并展现出利用光谱信息增强语义分割的潜力。本研究为未来基于HSI的ADAS感知评估，在现有带标注数据集上建立了基准SSM评价体系。然而，当前HSI数据集存在的局限性——如数据集规模有限、类别高度不平衡以及缺乏细粒度标注——仍是开发面向ADAS应用的鲁棒SSM的重要制约因素。