Introducing DLR Hysu - A Benchmark Dataset for Spectral Unmixing

Daniele Cerra, Miguel Pato, Kevin Alonso, Claas H. Köhler, Mathias Schneider, Raquel De los Reyes, Emiliano Carmona, Rudolf Richter, Franz Kurz, Rupert Müller, Peter Reinartz

Abstract: The DLR HyperSpectral Unmixing (DLR HySU) open benchmark dataset includes airborne hyperspectral and RGB imagery of targets of different materials and sizes on a homogeneous background, complemented by simultaneous ground-based reflectance measurements. The dataset allows assessing dimensionality estimation, endmember extraction with and without pure pixel assumption, and abundance estimation in the frame of spectral unmixing applications, enabling estimations at sub-pixel level. This paper presents the first works in the literature using the dataset, which demonstrate that DLR HySU is filling a gap regarding validation using real imaging spectrometer data with accurately measured targets.

External IDs:dblp:conf/igarss/CerraPAKSRCRKMR23