Spectral imaging collects and processes information along spatial and spectral coordinates
quantified in discrete voxels, which can be treated as a 3D spectral data cube. The spectral …

Accurate unsupervised classification of hyperspectral images (HSIs) is challenging and has
drawn widespread attention in remote sensing due to its inherent complexity. Although …

Image fusion aims at estimating a high-resolution spectral image from a low-spatial-
resolution hyperspectral image and a low-spectral-resolution multispectral image. In this …

The accurate segmentation of remotely sensed hyperspectral images has widespread
attention in the Earth observation and remote sensing communities. In the past decade, most …

In the compressive spectral imaging (CSI) framework, different architectures have been
proposed to recover high-resolution spectral images from compressive measurements …

Spectral image classification uses the huge amount of information provided by spectral
images to identify objects in the scene of interest. In this sense, spectral images typically …

Multi-sensor image can provide supplementary information, usually leading to better
performance in classification tasks. However, the general deep neural network-based multi …

Hyperspectral images (HSIs) are high-dimensional and complex images that provide rich
spectral information of the scenes. Image processing and remote sensing communities are …

Compressive spectral imaging (CSI) has emerged as an attractive compression and sensing
technique, primarily to sense spectral regions where traditional systems result in highly …

Spectral video has emerged as a non-invasive scientific tool to analyze the behavior of
dynamic scenes in high spectral resolution. In view of its importance, several compressive …