Hyperspectral image classification (HSIC) based on deep learning has always been a
research hot spot in the field of remote sensing. However, most of the classification models …

Deep learning has achieved many successes in the field of the hyperspectral image (HSI)
classification. Most of existing deep learning-based methods have no consideration of …

In this paper, a Capsulenet-based framework is proposed for extracting spectral and spatial
features for improving hyperspectral image classification. Unlike conventional strategies, the …

Due to its remarkable feature representation capability and high performance, convolutional
neural networks (CNN) have emerged as a popular choice for hyperspectral image (HSI) …

The unique characteristics of hyperspectral images (HSI) undoubtedly pose significant
categorization issues while providing a wealth of information. High redundancy and a lack of …

Deep-learning-based methods have displayed promising performance for hyperspectral
image (HSI) classification, due to their capacity of extracting deep features from HSI …

Recently, convolutional neural network (CNN) and graph convolutional network (GCN) have
been used widely for hyperspectral image (HSI) classification which, respectively, specialize …

In this article, we propose a convolutional neural network with mapping layers (MCNN) for
hyperspectral image (HSI) classification. The proposed mapping layers map the input patch …

Recently, the hyperspectral image (HSI) classification methods based on convolutional
neural networks (CNN) have developed rapidly with the advance of deep learning (DL) …

In this paper, we designed an end-to-end spectral-spatial residual network (SSRN) that
takes raw 3-D cubes as input data without feature engineering for hyperspectral image …