Hyperspectral image classification (HSIC) has become a hot research topic. Hyperspectral
imaging (HSI) has been widely used in a wide range of real-world application areas due to …

The effective combination of hyperspectral image (HSI) and light detection and ranging
(LiDAR) data can be used for land cover classification. Recently, deep-learning-based …

In recent years, deep learning algorithms, particularly convolutional neural networks
(CNNs), have significantly improved the performance of the hyperspectral image (HSI) …

Hyperspectral image (HSI) classification is commonly influenced by convolution neural
networks (CNNs). However, the large number of parameters and computational complexity …

Recently, many deep learning-based methods have been successfully applied to
hyperspectral image (HSI) classification. Nevertheless, training a satisfactory network …

Hyperspectral image classification is a challenging task due to the high dimensionality and
complex nature of hyperspectral data. In recent years, deep learning techniques have …

Deep learning (DL) has been widely applied into hyperspectral image (HSI) classification
owing to its promising feature learning and representation capabilities. However, limited by …

The advent of cloud computing and advanced processing technologies has elevated Deep
Learning (DL) as a leading method for Hyper-Spectral Imaging (HSI) classification …

As a well-known nonlinear tool, mathematical morphology (MM) is still active in image
processing. Benefiting from the fixed structuring element (SE), traditional MM (TMM) gets …

Signals from different modalities each have their own combination algebra which affects
their sampling processing. RGB is mostly linear; depth is a geometric signal following the …