Hyperspectral imaging is a non-destructive, nonpolluting, and fast technology, which can
capture up to several hundred images of different wavelengths and offer relevant spectral …

Tensor completion recovers missing entries of multiway data. The missing of entries could
often be caused during the data acquisition and transformation. In this paper, we provide an …

Kernel methods, eg, composite kernels (CKs) and spatial-spectral kernels (SSKs), have
been demonstrated to be an effective way to exploit the spatial-spectral information …

In this article, we propose a novel tensorial approach, namely, generalized tensor
regression, for hyperspectral image classification. First, a simple and effective classifier, ie …

Tensor network methods have been a key ingredient of advances in condensed matter
physics and have recently sparked interest in the machine learning community for their …

高光谱成像是遥感领域的一项先进技术, 它能够收集和处理来自不同波段的电磁光谱信息,
包括可见光, 近红外和红外波段. 由于高光谱成像技术能够检测到光谱信息的细微变化, 因此 …

This paper presents a tensor alignment (TA) based domain adaptation (DA) method for
hyperspectral image (HSI) classification. To be specific, HSIs in both domains are first …

Written by leading global experts, including pioneers in the field, the four-volume set on
Hyperspectral Remote Sensing of Vegetation, Second Edition, reviews existing state-of-the …

A novel sparse tensor dictionary learning algorithm and a convolutional neural network
(CNN) classification method based on this algorithm are proposed for hyperspectral image …

Previous studies have demonstrated that spatial information can provide significant
improvement for the accuracy hyperspectral image (HSI) classification. However, it remains …