# A Dataset for Evaluating Blood Detection in Hyperspectral Images

arXiv: https://arxiv.org/abs/2008.10254 · HTML: https://mlsanity.com/paper/2008.10254/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2020.

## Built on by (most cited first, 4 of 4)

- [HyperVision: A Channel-Adaptive Ground-Based Hyperspectral Vision Pre-trained Backbone](https://mlsanity.com/paper/2605.17286/) (2026)
- [Hyperspectral Imaging](https://mlsanity.com/paper/2508.08107/) (2025)
- [Projected Sliced Wasserstein Autoencoder-based Hyperspectral Images Anomaly Detection](https://mlsanity.com/paper/2112.11243/) (2021)
- [Hyperspectral image segmentation with a machine learning model trained using quantum annealer](https://mlsanity.com/paper/2503.01400/) (2025)

## Abstract (arXiv)

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.