The realization of efficient classification with limited labeled samples is a critical task in
hyperspectral image classification (HSIC). Convolutional neural networks (CNNs) have …

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 …

Deep learning algorithms have shown significant advantages in hyperspectral image (HSI)
classification. However, these algorithms usually require a large number of labeled samples …

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

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

In recent years, deep neural networks have been widely used for hyperspectral image (HSI)
classification and have shown excellent performance using numerous labeled samples. The …

Deep learning has recently attracted significant attention in the field of hyperspectral images
(HSIs) classification. However, the construction of an efficient deep neural network mostly …

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

The efficiently and effectively discriminative spectral–spatial feature representation is
essential for hyperspectral image (HSI) classification. However, most of the existing methods …

In recent years, convolutional neural networks (CNNs) have been widely used in the field of
hyperspectral image (HSI) classification and achieved good classification results due to their …