ML Anthology

A Hyperspectral and RGB Dataset for Building Façade Segmentation

Nariman Habili, Ernest Kwan, Weihao Li, Christfried Webers, Jeremy Oorloff, Mohammad Ali Armin, Lars Petersson
ECCVW 2022 pp. 258-267
doi:10.1007/978-3-031-25082-8_17 /eccvw/2022/habili2022eccvw-hyperspectral/

Abstract

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. Our dataset is publicly available at CSIRO data access portal .

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Cite

Text

Habili et al. "A Hyperspectral and RGB Dataset for Building Façade Segmentation." European Conference on Computer Vision Workshops, 2022. doi:10.1007/978-3-031-25082-8_17

Markdown

[Habili et al. "A Hyperspectral and RGB Dataset for Building Façade Segmentation." European Conference on Computer Vision Workshops, 2022.](https://mlanthology.org/eccvw/2022/habili2022eccvw-hyperspectral/) doi:10.1007/978-3-031-25082-8_17

BibTeX

@inproceedings{habili2022eccvw-hyperspectral,
  title     = {{A Hyperspectral and RGB Dataset for Building Façade Segmentation}},
  author    = {Habili, Nariman and Kwan, Ernest and Li, Weihao and Webers, Christfried and Oorloff, Jeremy and Armin, Mohammad Ali and Petersson, Lars},
  booktitle = {European Conference on Computer Vision Workshops},
  year      = {2022},
  pages     = {258-267},
  doi       = {10.1007/978-3-031-25082-8_17},
  url       = {https://mlanthology.org/eccvw/2022/habili2022eccvw-hyperspectral/}
}