Hyperspectral image analysis has become an important topic widely researched
by the remote sensing community. Classification and segmentation of such
imagery help understand the underlying materials within a scanned scene, since
hyperspectral images convey a detailed information captured in a number of
spectral bands. Although deep learning has established the state of the art in
the field, it still remains challenging to train well-generalizing models due
to the lack of ground-truth data. In this letter, we tackle this problem and
propose an end-to-end approach to segment hyperspectral images in a fully
unsupervised way. We introduce a new deep architecture which couples 3D
convolutional autoencoders with clustering. Our multi-faceted experimental
study---performed over benchmark and real-life data---revealed that our
approach delivers high-quality segmentation without any prior class labels.

本文提出一种全新的无监督式深度学习算法，采用 3D 卷积自编码器与聚类方法相结合的方式，实现高精密度的高光谱图像分割，并作了多方面实验验证。

使用 3D 卷积自编码器对高光谱图像进行无监督分割

Unsupervised Segmentation of Hyperspectral Images Using 3D Convolutional  Autoencoders

Pixel-wise classification, where each pixel is assigned to a predefined
class, is one of the most important procedures in hyperspectral image (HSI)
analysis. By representing a test pixel as a linear combination of a small
subset of labeled pixels, a sparse representation classifier (SRC) gives rather
plausible results compared with that of traditional classifiers such as the
support vector machine (SVM). Recently, by incorporating additional structured
sparsity priors, the second generation SRCs have appeared in the literature and
are reported to further improve the performance of HSI. These priors are based
on exploiting the spatial dependencies between the neighboring pixels, the
inherent structure of the dictionary, or both. In this paper, we review and
compare several structured priors for sparse-representation-based HSI
classification. We also propose a new structured prior called the low rank
group prior, which can be considered as a modification of the low rank prior.
Furthermore, we will investigate how different structured priors improve the
result for the HSI classification.

本文综述了用于基于稀疏表示的高光谱图像分类的多种结构稀疏先验方法，并提出了一种新的结构先验方法 —— 低秩群先验来进一步提高分类表现。