论文标题
用于评估高光谱图像中血液检测的数据集
A Dataset for Evaluating Blood Detection in Hyperspectral Images
论文作者
论文摘要
成像光谱对血红蛋白衍生物的敏感性使其成为检测血液的有前途的工具。但是，由于高光谱图像的复杂性和高维度，高光谱血液检测算法的发展是具有挑战性的。为了促进他们的发展，我们提出了一个新的高光谱血液检测数据集。该数据集是按照开放访问任务发布的，由多个检测方案组成，复杂程度不同。它允许测试与不同采集环境，背景类型，血液年龄以及其他类似血液样物质的存在相关的机器学习方法的性能。我们通过血液检测实验探索了数据集。我们根据众所周知的匹配滤镜检测器使用了高光谱目标检测算法。我们的结果和他们的讨论突出了高光谱数据中血液检测的挑战，并构成了进一步工作的参考。
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.
