Hyperspectral unmixing has been an important technique that estimates a set of
endmembers and their corresponding abundances from a hyperspectral image (HSI) …

Owing to the rapid development of sensor technology, hyperspectral (HS) remote sensing
(RS) imaging has provided a significant amount of spatial and spectral information for the …

Hyperspectral (HS) images are characterized by approximately contiguous spectral
information, enabling the fine identification of materials by capturing subtle spectral …

Classification and identification of the materials lying over or beneath the earth's surface
have long been a fundamental but challenging research topic in geoscience and remote …

As remote sensing (RS) data obtained from different sensors become available largely and
openly, multimodal data processing and analysis techniques have been garnering …

Hyperspectral (HS) imaging, also known as image spectrometry, is a landmark technique in
geoscience and remote sensing (RS). In the past decade, enormous efforts have been made …

Deep neural network has been extensively applied to hyperspectral image (HSI)
classification recently. However, its success is greatly attributed to numerous labeled …

This paper addresses the problem of semi-supervised transfer learning with limited cross-
modality data in remote sensing. A large amount of multi-modal earth observation images …

Owing to the powerful data representation ability of deep learning (DL) techniques,
tremendous progress has been recently made in hyperspectral image (HSI) classification …

Although deep neural networks (DNNs) have been widely applied to hyperspectral image
(HSI) denoising, most DNN-based HSI denoising methods are designed by stacking …