Planned intervention: On Wednesday April 3rd 05:30 UTC Zenodo will be unavailable for up to 2-10 minutes to perform a storage cluster upgrade.
Published August 14, 2020 | Version 1.0
Dataset Open

A Dataset for Evaluating Blood Detection in Hyperspectral Images

Description

The sensitivity of hyperspectral imaging (imaging spectroscopy) to haemoglobin derivatives makes it a promising tool for detection and classification of 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 consists of 14 hyperspectral images (ENVI format) of a mock-up scene containing blood and visually similar substances (e.g. artificial blood or tomato concentrate). Images were taken over a period of three weeks and differ in terms of background composition and lighting intensity. To facilitate the use of data, the dataset includes an annotation of classes: pixels where blood and similar substances are visible have been marked by the authors. The main intention behind the dataset is to serve as testing data for Machine Learning methods for hyperspectral target detection and classification.

Files

HyperBlood.zip

Files (2.3 GB)

Name Size Download all
md5:fb0eb022d05c90b0781550d9759a8f49
2.3 GB Preview Download

Additional details

Related works

Cites
Preprint: arXiv:2008.10254 (arXiv)
Is cited by
Journal article: 10.3390/s20226666 (DOI)
Is documented by
Journal article: 10.1016/j.forsciint.2021.110701 (DOI)
References
Software: https://github.com/iitis/HSI_blood_detection (URL)