Vision transformers (ViTs) have been trending in image classification tasks due to their
promising performance when compared with convolutional neural networks (CNNs). As a …

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 fusion of multisource remote sensing (RS) data has demonstrated significant potential in
target recognition and classification tasks. However, there is limited emphasis on capturing …

Hyperspectral image (HSI) has rich spatial–spectral information, high spectral correlation,
and large redundancy between information. Due to the sparse background distribution of …

Remote sensing (RS) imagery provides invaluable insights into characterizing the Earth's
land surface within the scope of Earth observation (EO). Technological advances in capture …

Hyperspectral images (HSIs) can provide rich spectral–spatial information that has been
widely utilized in many fields, such as national defense, mineralogy, and agriculture. Most of …

Wheat variety identification from hyperspectral images holds significant importance in both
fine breeding and intelligent agriculture. However, the discriminatory accuracy of some …

Graph convolutional networks (GCNs) have recently received increasing attention in
hyperspectral image (HSI) classification, benefiting from their superiority in conducting …

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

Hyperspectral image (HSI) spectral-spatial joint feature (FE) extraction methods generally
suffer from low feature retention and weak spatial–spectral dependence, which will lead to …