In hyperspectral image (HSI) classification, each pixel sample is assigned to a land-cover
category. In the recent past, convolutional neural network (CNN)-based HSI classification …

Hyperspectral image (HSI) denoising is a crucial preprocessing procedure for the
subsequent HSI applications. Unfortunately, though witnessing the development of deep …

In the convolutional neural network, the precise segmentation of small-scale objects and
object boundaries in remote sensing images is a great challenge. As the model gets deeper …

With the increasing maturity of optics and photonics, hyperspectral technology has also
greatly advanced. Hyperspectral images composed of hundreds of adjacent bands and …

Hyperspectral image super-resolution (HSI-SR) can be achieved by fusing a paired
multispectral image (MSI) and hyperspectral image (HSI), which is a prevalent strategy. But …

Hyperspectral image (HSI) classification is currently a hot topic in the field of remote sensing.
The goal is to utilize the spectral and spatial information from HSI to accurately identify land …

Hyperspectral image (HSI) classification refers to accurately corresponding each pixel in an
HSI to a land-cover label. Recently, the successful application of multiscale and multifeature …

Hyperspectral image (HSI) restoration is a challenging research area, covering a variety of
inverse problems. Previous works have shown the great success of deep learning in HSI …

Compared to natural images, hyperspectral images (HSIs) consist of a large number of
bands, with each band capturing different spectral information from a certain wavelength …

Low-rank tensor representation philosophy has enjoyed a reputation in many hyperspectral
image (HSI) low-level vision applications, but previous studies often failed to …