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

ABSTRACT Convolutional Neural Network (CNN) has developed hyperspectral image (HSI)
classification effectively. Although many CNN-based models can extract local features in …

In order to achieve accurate hyperspectral image (HSI) classification, the convolutional
neural network (CNN) has been extensively utilized. However, most existing patch-based …

Hyperspectral (HS) image classification has become an important research area. Although
previous work on HS image classification has achieved impressive results, finding a proper …

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 …

Hyperspectral image (HSI) classification is an essential task in remote sensing with
substantial practical significance. However, most existing convolutional neural network …

Convolutional neural networks (CNNs) have become one of the most popular tools to tackle
hyperspectral image (HSI) classification tasks. However, CNN suffers from the long-range …

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 …

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