PUNCH：基于正无标记分类的高光谱图像信息检索
PUNCH: Positive UNlabelled Classification based information retrieval in Hyperspectral images
摘要：
机载或卫星传感器拍摄的土地覆盖物高光谱图像提供了有关特定地点物质化学成分的丰富信息来源。这使得高光谱成像成为地球科学、土地覆盖研究以及军事和战略应用的重要工具。然而，标记训练样本的稀缺性和光谱特征的空间变异性是高光谱图像分类面临的两个最大挑战。为了解决这些问题，我们的目标是开发一个框架的材料不可知信息检索在高光谱图像的基础上积极无标签（PU）分类。给定一个高光谱场景，用户标记他/她正在寻找的材料的一些阳性样本，我们的目标是检索场景中查询材料的所有剩余实例。此外，我们要求系统同样适用于任何场景中的任何材料，而无需用户公开查询材料的身份。框架的这种材料不可知性为其提供了优越的泛化能力。我们探索两种替代方法来解决这个框架内的高光谱图像分类问题。第一种方法是基于非负风险估计的PU学习的高光谱数据的适应。第二种方法是基于一对所有的积极-消极的分类，其中的负面类近似采样使用一种新的光谱空间检索模型。我们提出了两个注释器模型-均匀和斑点-代表人类注释器的标签模式。我们比较了三个基准高光谱图像数据集-印度松树，帕维亚大学和萨利纳斯每个注释模型的性能所提出的算法。
英文摘要：
Hyperspectral images of land-cover captured by airborne or satellite-mounted sensors provide a rich source of information about the chemical composition of the materials present in a given place. This makes hyperspectral imaging an important tool for earth sciences, land-cover studies, and military and strategic applications. However, the scarcity of labeled training examples and spatial variability of spectral signature are two of the biggest challenges faced by hyperspectral image classification. In order to address these issues, we aim to develop a framework for material-agnostic information retrieval in hyperspectral images based on Positive-Unlabelled (PU) classification. Given a hyperspectral scene, the user labels some positive samples of a material he/she is looking for and our goal is to retrieve all the remaining instances of the query material in the scene. Additionally, we require the system to work equally well for any material in any scene without the user having to disclose the identity of the query material. This material-agnostic nature of the framework provides it with superior generalization abilities. We explore two alternative approaches to solve the hyperspectral image classification problem within this framework. The first approach is an adaptation of non-negative risk estimation based PU learning for hyperspectral data. The second approach is based on one-versus-all positive-negative classification where the negative class is approximately sampled using a novel spectral-spatial retrieval model. We propose two annotator models - uniform and blob - that represent the labelling patterns of a human annotator. We compare the performances of the proposed algorithms for each annotator model on three benchmark hyperspectral image datasets - Indian Pines, Pavia University and Salinas.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
