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

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 a significant foundation for remote sensing image
analysis, widely used in biology, aerospace, and other applications. Convolution neural …

Deep learning methods have shown great advantages in hyperspectral image (HSI)
classification tasks. In particular, convolutional neural network (CNN)-based methods for HSI …

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 been widely used in hyperspectral image (HSI)
classification tasks because of their excellent local spatial feature extraction capabilities …

Deep learning-based hyperspectral image (HSI) classification methods have recently
attracted significant attention. However, features captured by convolutional neural network …

This paper proposes a novel deep learning framework named bidirectional-convolutional
long short term memory (Bi-CLSTM) network to automatically learn the spectral-spatial …

In recent years, deep learning (DL) has been extensively used in the hyperspectral image
(HSI) classification. The representative method is the convolutional neural network (CNN) …

Recently, deep learning methods based on the combination of spatial and spectral features
have been successfully applied in hyperspectral image (HSI) classification. To improve the …