Hyperspectral and multispectral images (HS/MS) fusion and classification as an important
branch of data quality improvement and interpretation have attracted increasing attention in …

Convolutional neural networks (CNNs) have attracted increasing attention in the field of
multimodal cooperation. Recently, the adoption of CNN-based methods has achieved …

Owing to rich spectral and spatial information, hyperspectral image (HSI) can be utilized for
finely classifying different land covers. With the emergence of deep learning techniques …

The highly correlated spectral features and the limited training samples pose challenges in
hyperspectral image classification. In this article, to tackle the issues of end-to-end feature …

Transformer-based networks, which can well model the global characteristics of inputted
data using the attention mechanism, have been widely applied to hyperspectral image (HSI) …

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

Deep learning has emerged as a powerful method for hyperspectral image (HSI)
classification. However, a significant prerequisite for HSI classification using deep learning …

Hyperspectral image (HSI) classification has attracted significant interest among researchers
owing to its diverse practical applications. Convolutional neural networks (CNNs) have been …

Deep learning-based methods have shown promising outcomes in many fields. However,
the performance gain is always limited to a large extent in classifying hyperspectral image …

Protection of wetlands is important for ecosystem in recent years, and the classification of
wetland ground cover is the foundation of investigation and protection work. Probabilistic …