Spatial–spectral classification (SSC) has become a trend for hyperspectral image (HSI)
classification. However, most SSC methods mainly consider local information, so that some …

Deep learning (DL) has brought many new trends for hyperspectral image classification
(HIC). Graph neural networks (GNNs) are models that fuse DL and structured data. Although …

Transformer is a powerful tool for capturing long-range dependencies and has shown
impressive performance in hyperspectral image (HSI) classification. However, such power …

Transformer holds significance in deep learning (DL) research. Node embedding (NE) and
positional encoding (PE) are usually two indispensable components in a Transformer. The …

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

Although convolutional neural networks (CNNs) have shown superior performance to
traditional machine learning algorithms for hyperspectral image (HSI) classification tasks …

Currently, convolutional neural network (CNN) and vision transformer (ViT) are gradually
becoming the mainstream for hyperspectral image (HSI) classification. Although CNN and …

高光谱图像凭借其“图谱合一” 的特点逐渐在军事, 环境, 农业等方面发挥出重要作用. 但是,
由于传感器空间分辨率的限制以及地物分布的复杂多样性, 高光谱遥感图像中通常存在大量的 …

An important task of spectral unmixing is to recover the signatures of endmembers from a
hyperspectral dataset in which no pure signature is exposed. Most algorithms are based on …

Deep learning has made significant progress in hyperspectral image (HSI) classification,
and its powerful ability to automatically learn abstract features is well recognized. Recently …