Hyperspectral imaging has been used in a variety of geological applications since its advent
in the 1970s. In the last few decades, different techniques have been developed by …

Recently, many efforts have been concentrated on land use land cover (LULC) classification
due to rapid urbanization, environmental pollution, agriculture drought, frequent floods, and …

In recent years, deep-learning-based hyperspectral image (HSI) classification methods have
achieved significant development. The superior capability of feature extraction from these …

As a powerful visual model, convolutional neural networks (CNNs) have demonstrated
remarkable performance in various visual recognition problems, and attracted considerable …

Saliency detection has been a hot topic in recent years, and many efforts have been devoted
in this area. Unfortunately, the results of saliency detection can hardly be utilized in general …

Through imaging the same spatial area by hyperspectral sensors at different spectral
wavelengths simultaneously, the acquired hyperspectral imagery often contains hundreds of …

Deep learning has achieved great success in hyperspectral image classification. However,
when processing new hyperspectral images, the existing deep learning models must be …

This paper proposes a novel approach based on adaptive dimensionality reduction (ADR)
and a semi-supervised 3-D convolutional neural network (3-D CNN) for the spectro-spatial …

The rich information available in hyperspectral imagery not only poses significant
opportunities but also makes big challenges for material classification. Discriminative …

Band selection is an important preprocessing step for hyperspectral image processing.
Many valid criteria have been proposed for band selection, and these criteria model band …