[论文解读] Hyperspectral Unmixing: Ground Truth Labeling, Datasets, Benchmark Performances and Survey

Feiyun Zhu|arXiv (Cornell University)|Aug 17, 2017

Remote-Sensing Image Classification参考文献 99被引用 83

一句话总结

本文提出了一种用于高光谱混合分解（HU）的通用地真标注方法，概述了带有18个地真变体的15个真实HU数据集，提出将高光谱分类数据转换为HU所需的格式，并提供基准测试和代码以实现HU方法的可重复评估。

ABSTRACT

Hyperspectral unmixing (HU) is a very useful and increasingly popular preprocessing step for a wide range of hyperspectral applications. However, the HU research has been constrained a lot by three factors: (a) the number of hyperspectral images (especially the ones with ground truths) are very limited; (b) the ground truths of most hyperspectral images are not shared on the web, which may cause lots of unnecessary troubles for researchers to evaluate their algorithms; (c) the codes of most state-of-the-art methods are not shared, which may also delay the testing of new methods. Accordingly, this paper deals with the above issues from the following three perspectives: (1) as a profound contribution, we provide a general labeling method for the HU. With it, we labeled up to 15 hyperspectral images, providing 18 versions of ground truths. To the best of our knowledge, this is the first paper to summarize and share up to 15 hyperspectral images and their 18 versions of ground truths for the HU. Observing that the hyperspectral classification (HyC) has much more standard datasets (whose ground truths are generally publicly shared) than the HU, we propose an interesting method to transform the HyC datasets for the HU research. (2) To further facilitate the evaluation of HU methods under different conditions, we reviewed and implemented the algorithm to generate a complex synthetic hyperspectral image. By tuning the hyper-parameters in the code, we may verify the HU methods from four perspectives. The code would also be shared on the web. (3) To provide a standard comparison, we reviewed up to 10 state-of-the-art HU algorithms, then selected the 5 most benchmark HU algorithms, and compared them on the 15 real hyperspectral datasets. The experiment results are surely reproducible; the implemented codes would be shared on the web.

研究动机与目标

• 提出一种用于HU的通用标注方法，以创建端元和丰度的地真。
• 总结并分享15个真实的HU图像，含18个地真变体，以标准化评估。
• 提供一个合成HU图像生成方法并分享其代码以确保可重复性。
• 对这15个真实HU数据集中的最前沿HU算法进行评审与基准测试。

提出的方法

• 使用第 IV-A 和 IV-B 节的方法对 HU 的端元和丰度进行标注。
• 提供对标注结果的评估框架（第 IV-C 节）。
• 将 HyC 基准数据集转换为适合HU的格式（第 IV-D 节）。
• 生成并分享一个复杂的合成HU图像（第 VI 节）。
• 评审最多10种HU方法并实现5种作为基准算法（第 III 节）。
• 在这15个真实HU数据集上比较这5种基准HU方法（第 VII 节）。

实验结果

研究问题

• RQ1 我们如何跨多个数据集系统性地标注HU的端元和丰度？
• RQ2 HyC基准数据集是否可以转换为适用于HU的一致数据集，以实现标准化评估？
• RQ3 复杂合成HU图像对方法基准测试的影响是什么？
• RQ4 哪些HU方法在真实HU数据集上的基准表现最佳？
• RQ5 所提供的代码和地真数据集是否足以实现可重复的HU评估？

主要发现

• 一种通用标注方法对端元和丰度进行标注，并提供对标注结果的评估（第 IV 节）。
• 从15个真实HU图像创建了18个地真变体，作为HU评估的共享标准数据集。
• 生成了一个复杂的合成HU图像，以实现多视角的HU测试，并共享其代码。
• 本文评审了10种HU方法，并为5种最先进的方法提供了代码。
• 在这15个真实HU数据集上实现并评估了五种基准HU方法（可重复的结果）。