Hyperspectral Imaging (HSI) provides detailed spectral information and has been utilised in many real-world applications. This work introduces an HSI dataset of building facades in a light industry environment with the aim of classifying different building materials in a scene. The dataset is called the Light Industrial Building HSI (LIB-HSI) dataset. This dataset consists of nine categories and 44 classes. In this study, we investigated deep learning based semantic segmentation algorithms on RGB and hyperspectral images to classify various building materials, such as timber, brick and concrete.
翻译:超光谱成像(HSI)提供详细的光谱信息,并用于许多现实世界应用中,这项工作引入了在轻工业环境中建筑外形的HSI数据集,目的是在现场对不同的建筑材料进行分类。数据集称为轻工业建筑HSI(LIB-HSI)数据集。该数据集由九类和44类组成。在本研究中,我们研究了基于深学习的RGB和超光谱图像的语系分离算法,以对木材、砖块和混凝土等各种建筑材料进行分类。