PCA BAND SELECTION METHOD FOR A HYPERSPECTRAL SENSORS ONBOARD AN UAV

Centeno, J. A. S.; Kern, J.; Mitishita, E. A.; Palma, M. E. J.

The development of light and small sensors, like Lidar and hyperspectral sensors, has gained popularity over the last few years. In this paper we present the experience of UFPR (Brazil), in collaboration with KIT (Germany), on the use of a UAV system carrying a hyperspectral sensor for land cover studies. The sensors were integrated with the traditional IMU-GNSS systems to record data from a quadricopter. The study focuses on band selection, aiming at reducing computational effort and statistical limitations. For this purpose, the principal components of the multispectral image are computed. The best principal components are then selected according to the explained original variance, as described by the relative size of the eigenvalues. Then, each principal component is analyzed searching for contrasting spectral regions, described by consecutive positive and negative coefficients. The most representative band of each spectral region is the selected according to its information contents and contribution to the computation of the respective eigenvectors. The method is tested using images collected with the FireflEYE 185 Cubert camera with 125 channels in the wavelength between 450 nm and 950 nm, flying over the experimental Canguiri farm in Curitiba, Brazil. Finally, we discuss the advantages of the method and its limitations.

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Centeno, J. A. S. / Kern, J. / Mitishita, E. A. / et al: PCA BAND SELECTION METHOD FOR A HYPERSPECTRAL SENSORS ONBOARD AN UAV. 2020. Copernicus Publications.

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