Various machine learning and deep learning techniques have been proposed for
classification purposes in the case of hyperspectral imaging. Among all the machine …

In deep learning-based hyperspectral remote sensing image classification tasks, random
sampling strategies are typically used to train model parameters for testing and evaluation …

With the continuous progress of computer deep learning technology, convolutional neural
network (CNN), as a representative approach, provides a unique solution for hyperspectral …

The land-covers within an observed remote sensing scene are usually of different scales;
therefore, the ensemble of multi-scale information is a commonly used strategy to achieve …

Higher dimensionality, Hughes phenomenon, spatial resolution of image data, and
presence of mixed pixels are the main challenges in a multi-spectral image classification …

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 …

Deep transfer-learning-based change detection methods are dependent on the availability
of sensor-specific pretrained feature extractors. Such feature extractors are not always …

Active Learning (AL) for Hyperspectral Image Classification (HSIC) has been extensively
studied. However, the traditional AL methods do not consider randomness among the …

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

In recent years, the hyperspectral classification algorithm based on deep learning has
received widespread attention, but the existing network models have higher model …