Hyperspectral images (HSI) are obtained from hyperspectral imaging sensors to capture the
object's information in hundreds of spectral bands. However, how to make full advantage of …

Spatial–spectral classification (SSC) has become a trend for hyperspectral image (HSI)
classification. However, most SSC methods mainly consider local information, so that some …

Transformers have intrigued the vision research community with their state-of-the-art
performance in natural language processing. With their superior performance, transformers …

Deep learning (DL) has brought many new trends for hyperspectral image classification
(HIC). Graph neural networks (GNNs) are models that fuse DL and structured data. Although …

Deep learning (DL) has heavily impacted the data-intensive field of remote sensing.
Autoencoders are a type of DL methods that have been found to be powerful for blind …

Spectral unmixing is central when analyzing hyperspectral data. To accomplish this task,
physics-based methods have become popular because, with their explicit mixing models …

Due to the powerful and automatic representation capabilities, deep learning (DL)
techniques have made significant breakthroughs and progress in hyperspectral unmixing …

Hyperspectral unmixing plays an important role in hyperspectral image processing and
analysis. It aims to decompose mixed pixels into pure spectral signatures and their …

Characterized by tremendous spectral information, hyperspectral image is able to detect
subtle changes and discriminate various change classes for change detection. The recent …

The pixels of remote images often contain more than one distinct material (mixed pixels),
and so their spectra are characterized by a mixture of spectral signals. Since 1971, a shared …