Hyperspectral remote sensing has tremendous potential for monitoring land cover and water
bodies from the rich spatial and spectral information contained in the images. It is a time and …

The performance of deep convolutional neural networks has been significantly improved in
recent years as a result of additional attention mechanisms applied to the standard …

Using deep learning to classify hyperspectral image (HSI) with only a few labeled samples
available is a challenge. Recently, the knowledge distillation method based on soft label …

Hyperspectral imaging (HSI) is widely used in various fields owing to its rich spectral
information. Nonetheless, the high dimensionality of HSI and the limited number of labeled …

The joint classification of hyperspectral image (HSI) and Light Detection and Ranging
(LiDAR) data can provide complementary information for each other, which has become a …

In Hyperspectral images (HSI), dimensionality reduction methods (DRM) play a critical role
in reducing the input data dimension and complexity. As much as the deep learning …

In this research, a deep learning approach for hyperspectral image (HSI) classification was
developed, incorporating attention mechanisms, multiscale feature learning, and utilization …

Reasonable fusion of multimodal data can increase the accuracy of remote sensing
classification. In this article, an effective morphological convolution and attention calibration …

In recent years, hyperspectral image (HSI) classification (HSIC) methods that use deep
learning have proved to be effective. In particular, the utilization of convolutional neural …

Hyperspectral image (HSI) classification is a classical task in remote sensing image
analysis. With the development of deep learning, schemes based on deep learning have …