论文标题
tas-nir：在非结构化室外环境中用于细粒语义细分的VIS+NIR数据集
TAS-NIR: A VIS+NIR Dataset for Fine-grained Semantic Segmentation in Unstructured Outdoor Environments
论文作者
论文摘要
基于可见色谱（VIS）和近红外光谱（NIR）的配对图像的植被指数已被广泛用于遥感应用中。这些植被指数扩展了它们在非结构化室外环境中的自动驾驶中的应用。在该领域，我们可以将传统的植被指数（例如标准化差异植被指数（NDVI）和增强的植被指数（EVI）与预先训练的可用VIS数据集预先培训的卷积神经网络（CNN）相结合。通过将重点放在学习校准的CNN输出上，我们可以为融合已知的手工制作的图像特征的方法以及对不同域的CNN预测。该方法在非结构化室外环境中的语义注释图像的VIS+NIR数据集上进行评估。该数据集可在Mucar3.de/IROS2022-PPNIV-TAS-NIR上找到。
Vegetation Indices based on paired images of the visible color spectrum (VIS) and near infrared spectrum (NIR) have been widely used in remote sensing applications. These vegetation indices are extended for their application in autonomous driving in unstructured outdoor environments. In this domain we can combine traditional vegetation indices like the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) with Convolutional Neural Networks (CNNs) pre-trained on available VIS datasets. By laying a focus on learning calibrated CNN outputs, we can provide an approach to fuse known hand-crafted image features with CNN predictions for different domains as well. The method is evaluated on a VIS+NIR dataset of semantically annotated images in unstructured outdoor environments. The dataset is available at mucar3.de/iros2022-ppniv-tas-nir.
