Spectral super-resolution (sSR) is a very important technique to obtain hyperspectral images
from only RGB images, which can effectively overcome the high acquisition cost and low …

Hyperspectral image (HSI) contains hundreds of adjacent spectral bands, which can
effectively differentiate the region of interest. Nevertheless, many irrelevant and highly …

Over the past few years, single image super-resolution (SR) has become a hotspot in the
remote sensing area, and numerous methods have made remarkable progress in this …

In practical applications, multi-view data depicting objects from assorted perspectives can
facilitate the accuracy increase of learning algorithms. However, given multi-view data, there …

Multi-view clustering divides similar objects into the same class through using the fused
multiview information. Most multi-view clustering methods obtain clustering result by only …

As an effective manner to reduce data redundancy and processing inconvenience,
hyperspectral band selection aims to select a subset of informative and discriminative bands …

Multi-view clustering focuses on exploring cluster structures among multiple views and is an
effective approach to achieve multi-view information fusion without requiring label …

With the increasing spectral dimension of hyperspectral images (HSI), how correctly choose
bands based on band correlation and information has become more significant, but also …

The fusion of hyperspectral and light detection and range (LiDAR) data has been an active
research topic. Existing fusion methods have ignored the high-dimensionality and …

Multi-view subspace clustering has attracted great attention due to its ability to explore data
structure by utilizing complementary information from different views. Most of existing …