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。

概率深度度量学习用于高光谱图像分类

Probabilistic Deep Metric Learning for Hyperspectral Image Classification

Image fusion combines data from different heterogeneous sources to obtain
more precise information about an underlying scene. Hyperspectral-multispectral
(HS-MS) image fusion is currently attracting great interest in remote sensing
since it allows the generation of high spatial resolution HS images,
circumventing the main limitation of this imaging modality. Existing HS-MS
fusion algorithms, however, neglect the spectral variability often existing
between images acquired at different time instants. This time difference causes
variations in spectral signatures of the underlying constituent materials due
to different acquisition and seasonal conditions. This paper introduces a novel
HS-MS image fusion strategy that combines an unmixing-based formulation with an
explicit parametric model for typical spectral variability between the two
images. Simulations with synthetic and real data show that the proposed
strategy leads to a significant performance improvement under spectral
variability and state-of-the-art performance otherwise.

本文介绍了一种新的 HS-MS 图像融合策略，该策略将不混合基础方法与典型光谱变异的显式参数模型相结合，模拟表明该策略在光谱变异下可以显著提高性能，并表现出最先进的性能。