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

Denoising is a crucial preprocessing step for hyperspectral images (HSIs) due to noise
arising from intraimaging mechanisms and environmental factors. Long-range spatial …

Hyperspectral images (HSIs) often suffer from noise arising from both intraimaging
mechanisms and environmental factors. Leveraging domain knowledge specific to HSIs …

Low-light remote sensing (RS) images generally feature high resolution and high spatial
complexity, with continuously distributed surface features in space. This continuity in scenes …

In hyperspectral images (HSIs), both local and nonlocal features play crucial roles in
classification tasks. Vision transformers (VITs) can extract nonlocal features through …

In the hyperspectral image (HSI) classification task, every HSI pixel is labeled as a specific
land cover category. Although convolutional neural network (CNN)-based HSI classification …

Hyperspectral image (HSI) classification has recently reached its performance bottleneck.
Multimodal data fusion is emerging as a promising approach to overcome this bottleneck by …

In the past decade, deep learning methods have proven to be highly effective in the
classification of hyperspectral images (HSI), consistently outperforming traditional …

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

Image gradients contain crucial information in the images. However, the gradient information
of low-light images is often concealed in darkness and is susceptible to noise contamination …