Exponential Features in the Fourier Domain for Prisma Hyperspectral Image Segmentation

The advances in the field of remote sensing for Earth Observation allow many applications like precision agriculture, forest monitoring, to name a few. Hyperspectral imaging is the technique that offers a high spectral resolution offering such applications more information about the Earth surface, but the data volume to be stored and processed increases too. A reason for the increased data volume is the high intrinsic variability of spectral reflectance curves. In this paper we propose a feature extraction method for dimensionality reduction based on fitting a negative exponential function to the Fourier spectrum of each spectral reflectance curve. The hypothesis is that extracting an exponential profile of the Fourier amplitude spectrum, thus reducing the variability of the spectral signatures, will possibly impact the segmentation approach. We further implement a segmentation algorithm based on the extracted features and assess its performance.