This paper proposes a probabilistic deep metric learning (PDML) framework for
hyperspectral image classification, which aims to predict the category of each
pixel for an image captured by hyperspectral sensors. The core problem for
hyperspectral image classification is the spectral variability between
intraclass materials and the spectral similarity between interclass materials,
motivating the further incorporation of spatial information to differentiate a
pixel based on its surrounding patch. However, different pixels and even the
same pixel in one patch might not encode the same material due to the low
spatial resolution of most hyperspectral sensors, leading to an inconsistent
judgment of a specific pixel. To address this issue, we propose a probabilistic
deep metric learning framework to model the categorical uncertainty of the
spectral distribution of an observed pixel. We propose to learn a global
probabilistic distribution for each pixel in the patch and a probabilistic
metric to model the distance between distributions. We treat each pixel in a
patch as a training sample, enabling us to exploit more information from the
patch compared with conventional methods. Our framework can be readily applied
to existing hyperspectral image classification methods with various network
architectures and loss functions. Extensive experiments on four widely used
datasets including IN, UP, KSC, and Houston 2013 datasets demonstrate that our
framework improves the performance of existing methods and further achieves the
state of the art. Code is available at: this https URL.

本文提出了一种概率深度度量学习（PDML）框架，用于高光谱图像分类，通过将空间信息纳入模型并利用更多信息以差异化像素，该框架可应用于现有方法，并在四个广泛使用的数据集上获得技术优势。