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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References
Adão, T., et al.: Hyperspectral imaging: a review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sens. 9(11), 1110 (2017)
Chakraborty, T., Trehan, U.: Spectralnet: exploring spatial-spectral waveletcnn for hyperspectral image classification (2021)
Dai, M., Ward, W.O., Meyers, G., Tingley, D.D., Mayfield, M.: Residential building facade segmentation in the urban environment. Build. Environ. 199, 107921 (2021)
Dale, L.M., et al.: Hyperspectral imaging applications in agriculture and AGRO-food product quality and safety control: a review. Appl. Spectrosc. Rev. 48(2), 142–159 (2013)
Feng, Y.Z., Sun, D.W.: Application of hyperspectral imaging in food safety inspection and control: a review. Crit. Rev. Food Sci. Nutr. 52(11), 1039–1058 (2012)
Graña, M., Veganzons, M., Ayerdi, B.: Hyperspectral remote sensing scenes. http://ehu.eus/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes
Habili, N., Oorloff, J.: Scyllarus™: from research to commercial software. In: Proceedings of the ASWEC 2015 24th Australasian Software Engineering Conference, pp. 119–122 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2017)
Korč, F., Förstner, W.: eTRIMS Image Database for interpreting images of man-made scenes (TR-IGG-P-2009-01) (2009). http://www.ipb.uni-bonn.de/projects/etrims_db/
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Lu, G., Fei, B.: Medical hyperspectral imaging: a review. J. Biomed. Opt. 19(1), 010901 (2014)
Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., Johnson, B.A.: Deep learning in remote sensing applications: a meta-analysis and review. ISPRS J. Photogramm. Remote. Sens. 152, 166–177 (2019)
Riemenschneider, H., Krispel, U., Thaller, W., Donoser, M., Havemann, S., Fellner, D., Bischof, H.: Irregular lattices for complex shape grammar facade parsing. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1640–1647. IEEE (2012)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d-2-d CNN feature hierarchy for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 17(2), 277–281 (2019)
Roy, S.K., Manna, S., Song, T., Bruzzone, L.: Attention-based adaptive spectral-spatial kernel ResNet for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 59(9), 7831–7843 (2020)
Teboul, O., Kokkinos, I., Simon, L., Koutsourakis, P., Paragios, N.: Shape grammar parsing via reinforcement learning. In: CVPR 2011, pp. 2273–2280. IEEE (2011)
Tyleček, R., Šára, R.: Spatial pattern templates for recognition of objects with regular structure. In: Weickert, J., Hein, M., Schiele, B. (eds.) GCPR 2013. LNCS, vol. 8142, pp. 364–374. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40602-7_39
Zhang, S., Deng, Q., Ding, Z.: Hyperspectral image segmentation based on graph processing over multilayer networks. arXiv preprint. arXiv:2111.15018 (2021)
Zhao, J., Hu, L., Dong, Y., Huang, L., Weng, S., Zhang, D.: A combination method of stacked autoencoder and 3d deep residual network for hyperspectral image classification. Int. J. Appl. Earth Obs. Geoinf. 102, 102459 (2021)
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Habili, N. et al. (2023). A Hyperspectral and RGB Dataset for Building Façade Segmentation. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13807. Springer, Cham. https://doi.org/10.1007/978-3-031-25082-8_17
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