Recently, hyperspectral image (HSI) classification methods based on convolutional neural
networks (CNN) have shown impressive performance. However, HSI classification still faces …

Hyperspectral images (HSIs) are characterized by high spatial resolution and are rich in
spectral information. In the process of HSI classification, the extraction of spectral–spatial …

Convolutional neural networks (CNN) are becoming increasingly popular in modern remote
sensing image classification tasks and have exhibited excellent results. For the existing …

Deep learning has been utilized for hyperspectral image (HSI) classification in recent years,
with notable performance improvements. In particular, convolutional neural networks …

In recent years, convolutional neural networks (CNNs) have been introduced for pixel-wise
hyperspectral image (HSI) classification tasks. However, some problems of the CNNs are …

Recently, hyperspectral image classification based on deep learning has achieved
considerable attention. Many convolutional neural network classification methods have …

Convolutional neural networks (CNNs) have demonstrated impressive performance and
have been broadly applied in hyperspectral image (HSI) classification. However, two …

Convolutional neural network (CNN) has been successfully introduced to hyperspectral
image (HSI) classification and achieved effective performance. With the depth of the CNN …

Feature representation is crucial for hyperspectral image (HSI) classification. However,
existing convolutional neural network (CNN)-based methods are limited by the convolution …

Hyperspectral image (HSI) classification is one of the most crucial tasks in remote sensing
processing. The attention mechanism is preferable to a convolutional neural network (CNN) …