Abstract
Abstract. 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.
Highlights
The use of UAV, supported by advances in image processing and photogrammetry, is well established in Cartography
The Principal Component Transform provides a set of uncorrelated variables, their computation requires the use of all available bands, including spectral neighbouring bands that are highly correlated
In this paper it was introduced a feature extraction approach based on the Principal Component Transform and the analysis of the eigenvectors coefficients
Summary
The use of UAV (unmanned aerial vehicles), supported by advances in image processing and photogrammetry, is well established in Cartography. This practice is only possible because of the reduction of imaging devices size, weight and energy consumption, as well as advances in battery size and power(Colomina and Molina, 2014). Hyperspectral remote sensing is based on the measurement of electromagnetic radiation in a high number of narrow, contiguous spectral bands. In this context, the narrowness and contiguous nature of the measurements is more relevant than the number of bands
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