  The sensitivity of imaging spectroscopy to haemoglobin derivatives makes it a
promising tool for detecting blood. However, due to complexity and high
dimensionality of hyperspectral images, the development of hyperspectral blood
detection algorithms is challenging. To facilitate their development, we
present a new hyperspectral blood detection dataset. This dataset, published in
accordance to open access mandate, consist of multiple detection scenarios with
varying levels of complexity. It allows to test the performance of Machine
Learning methods in relation to different acquisition environments, types of
background, age of blood and presence of other blood-like substances. We
explored the dataset with blood detection experiments. We used hyperspectral
target detection algorithm based on the well-known Matched Filter detector. Our
results and their discussion highlight the challenges of blood detection in
hyperspectral data and form a reference for further works.
