论文标题
使用机器学习方法与遗传算法结合在转导和诱导的场景中的高光谱分类
Hyperspectral classification of blood-like substances using machine learning methods combined with genetic algorithms in transductive and inductive scenarios
论文作者
论文摘要
这项研究的重点是将遗传算法（GA）应用于高光谱图像分类中的模型和频带选择。我们在两种情况下，使用七个具有血液和五种视觉上类似物质的高光谱图像和五种视觉上类似物质的七个高光谱图像的数据集来测试GAPTIMETIMET的分类器：当训练和测试数据来自相同的图像以及它们来自不同的图像时，由于显着的光谱差异，这是一项更具挑战性的任务。在我们的实验中，我们通过网格搜索将GA与经典模型优化进行比较。我们的结果表明，基于GA的模型优化可以减少频带的数量，并创建一个精确的分类器，以优于基于GS的参考模型，前提是在模型优化期间，它可以访问类似于测试数据的示例。我们通过实验强调验证集的重要性来说明这一点。
This study is focused on applying genetic algorithms (GA) to model and band selection in hyperspectral image classification. We use a forensic-inspired data set of seven hyperspectral images with blood and five visually similar substances to test GA-optimised classifiers in two scenarios: when the training and test data come from the same image and when they come from different images, which is a more challenging task due to significant spectra differences. In our experiments we compare GA with a classic model optimisation through grid search. Our results show that GA-based model optimisation can reduce the number of bands and create an accurate classifier that outperforms the GS-based reference models, provided that during model optimisation it has access to examples similar to test data. We illustrate this with experiment highlighting the importance of a validation set.
