Abstract
Hyperspectral imaging, which collects rich spectral and spatial information, is a powerful Earth vision method and has many applications. As the data structure is highly complex, the key problem of hyperspectral image processing is in extracting the useful information we want. Traditional feature extraction methods are designed to this end; however, they undergo severe limitations. Most of them are designed mathematically instead of physically and ignore the fact that the changes in physical imaging conditions have a significant influence on the spectra intensity observed. In this chapter, we try to analyze the information contained in hyperspectral images (HSIs) from the perspective of the hyperspectral imaging principle, and propose a novel method of extracting reflectance and surface normals from HSIs.
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Acknowledgements
This work was supported in part by the National Science Fund for Excellent Young Scholars under Grant 61522107 and the National Natural Science Foundation of key international cooperation under the Grant 61720106002. This work was also supported by a key research and development project of the Ministry of Science and Technology (No.2017YFC1405100). (Corresponding author: Yanfeng Gu.)
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Xiang, W., Jin, X., Gu, Y. (2019). Reflectance and Surface Normals from a Single Hyperspectral Image. In: Quinto, E., Ida, N., Jiang, M., Louis, A. (eds) The Proceedings of the International Conference on Sensing and Imaging, 2018. ICSI 2018. Lecture Notes in Electrical Engineering, vol 606. Springer, Cham. https://doi.org/10.1007/978-3-030-30825-4_10
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DOI: https://doi.org/10.1007/978-3-030-30825-4_10
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