论文标题
高光谱图像的主动扩散和VCA辅助图像分割
Active Diffusion and VCA-Assisted Image Segmentation of Hyperspectral Images
论文作者
论文摘要
高光谱图像编码可以通过机器学习算法来利用的丰富结构。本文介绍了主动材料歧视的主动扩散和VCA辅助图像分割（咨询）。建议选择与高光谱图像中其他高纯度高密度像素相距远距离扩散距离（数据依赖的度量）的高密度像素。这些像素的地面真实标签被查询并传播到其余图像。该咨询的积极学习算法被证明非常胜过其完全无监督的聚类算法对应物，这表明掺入少量精心挑选的地面真相标签可以导致高光谱图像中的实质材料歧视。
Hyperspectral images encode rich structure that can be exploited for material discrimination by machine learning algorithms. This article introduces the Active Diffusion and VCA-Assisted Image Segmentation (ADVIS) for active material discrimination. ADVIS selects high-purity, high-density pixels that are far in diffusion distance (a data-dependent metric) from other high-purity, high-density pixels in the hyperspectral image. The ground truth labels of these pixels are queried and propagated to the rest of the image. The ADVIS active learning algorithm is shown to strongly outperform its fully unsupervised clustering algorithm counterpart, suggesting that the incorporation of a very small number of carefully-selected ground truth labels can result in substantially superior material discrimination in hyperspectral images.
