Hyperspectral sensors enable the study of the chemical properties of scene
materials remotely for the purpose of identification, detection, and chemical
composition analysis of objects in the environment. Hence, hyperspectral images
captured from earth observing satellites and aircraft have been increasingly
important in agriculture, environmental monitoring, urban planning, mining, and
defense. Machine learning algorithms due to their outstanding predictive power
have become a key tool for modern hyperspectral image analysis. Therefore, a
solid understanding of machine learning techniques have become essential for
remote sensing researchers and practitioners. This paper reviews and compares
recent machine learning-based hyperspectral image analysis methods published in
literature. We organize the methods by the image analysis task and by the type
of machine learning algorithm, and present a two-way mapping between the image
analysis tasks and the types of machine learning algorithms that can be applied
to them. The paper is comprehensive in coverage of both hyperspectral image
analysis tasks and machine learning algorithms. The image analysis tasks
considered are land cover classification, target detection, unmixing, and
physical parameter estimation. The machine learning algorithms covered are
Gaussian models, linear regression, logistic regression, support vector
machines, Gaussian mixture model, latent linear models, sparse linear models,
Gaussian mixture models, ensemble learning, directed graphical models,
undirected graphical models, clustering, Gaussian processes, Dirichlet
processes, and deep learning. We also discuss the open challenges in the field
of hyperspectral image analysis and explore possible future directions.

本文综述和比较了最近发表在文献中的基于机器学习的高光谱图像分析方法，将方法按图像分析任务和机器学习算法类型进行组织，并提出了一种两向映射。文章全面涵盖了高光谱图像分析任务和机器学习算法，涵盖的图像分析任务包括土地覆盖分类、目标检测、反混合和物理参数估计，涵盖的机器学习算法包括高斯模型、线性回归、逻辑回归、支持向量机、混合高斯模型、稀疏线性模型、高斯混合模型、集成学习、有向图模型、无向图模型、聚类、高斯过程、狄利克雷过程和深度学习。同时，文章也讨论了高光谱图像分析领域的挑战和可能的未来方向。

基于机器学习的高光谱图像分析：一项调查

Machine learning based hyperspectral image analysis: A survey

Remote sensing hyperspectral sensors collect large volumes of high
dimensional spectral and spatial data. However, due to spectral and spatial
redundancy the true hyperspectral signal lies on a subspace of much lower
dimension than the original data. The identification of the signal subspace is
a very important first step for most hyperspectral algorithms. In this paper we
investigate the important problem of identifying the hyperspectral signal
subspace by minimizing the mean squared error (MSE) between the true signal and
an estimate of the signal. Since the MSE is uncomputable in practice, due to
its dependency on the true signal, we propose a method based on the Stein's
unbiased risk estimator (SURE) that provides an unbiased estimate of the MSE.
The resulting method is simple and fully automatic and we evaluate it using
both simulated and real hyperspectral data sets. Experimental results shows
that our proposed method compares well to recent state-of-the-art subspace
identification methods.

本研究针对遥感高光谱数据采集过程中存在的信号子空间识别问题，通过优化均方误差提出了一种基于 Stein's 无偏风险估计的方法，该方法简单自动且在模拟和真实高光谱数据集中均表现良好。