In recent years, deep-learning-based hyperspectral image (HSI) classification methods have
achieved significant development. The superior capability of feature extraction from these …

The joint hyperspectral image (HSI) and light detection and ranging (LiDAR) data
classification aims to interpret ground objects at more detailed and precise level. Although …

Convolutional neural networks (CNNs) have been widely employed for hyperspectral image
(HSI) classification due to their powerful ability to extract local spatial features. However …

The effective utilization of hyperspectral image (HSI) and light detection and ranging
(LiDAR) data is essential for land cover classification. Recently, deep learning-based …

Hyperspectral image (HSI) classification pertains to the task of assigning a single label to
each pixel by analyzing its spectral–spatial characteristics. Convolutional Neural Networks …

With more detailed spatial information being represented in very-high-resolution (VHR)
remote sensing images, stringent requirements are imposed on accurate image …

In recent years, Transformer-based change detection (CD) in remote sensing images has
achieved significant advances, making it an emerging hot research topic. However, the …

Hyperspectral target detection (HTD) aims to identifying targets within a hyperspectral image
(HSI) based on provided target spectra. In the current HTD field, representation-based …

The deep learning method has achieved great success in hyperspectral image
classification, but the lack of labeled training samples still restricts the development and …

Linear spectral unmixing is an important technique in the analysis of mixed pixels in
hyperspectral images. In recent years, deep learning-based methods have been garnering …