Dual|Coupled CNN|GCN|Based Classification for Hyperspectral ...

Dual-Coupled CNN-GCN-Based Classification for Hyperspectral ...

Deep learning techniques have brought substantial performance gains to remote sensing image classification. Among them, convolutional neural networks (CNN) ...

Dual-Coupled CNN-GCN-Based Classification for Hyperspectral ...

Deep learning techniques have brought substantial performance gains to remote sensing image classification. Among them, convolutional neural ...

Dual-Coupled CNN-GCN-Based Classification for Hyperspectral ...

These different features make it possible to classify remote sensing images finely. In addition, hyperspectral images and light detection and ...

Dual Interactive Graph Convolutional Networks for Hyperspectral ...

Dual Interactive Graph Convolutional Networks for Hyperspectral Image Classification. Abstract: Recently, graph convolutional network (GCN) ...

Dual Graph Convolutional Network for Hyperspectral Image ...

Dual Graph Convolutional Network for Hyperspectral Image Classification With Limited Training Samples ... GCN for HSI classification.

Dual Interactive Graph Convolutional Networks for Hyperspectral ...

Index Terms—Hyperspectral image classification, graph con- volutional network (GCN), deep learning, graph refinement. I. INTRODUCTION. This work was supported ...

Hyperspectral image classification using graph convolutional network

This paper presents a comprehensive review of GCN-based hyperspectral image classification methods. The review covers five aspects.

Dual-stream spectral-spatial convolutional neural network for ...

A comparative study of how CNN classification performance is affected by hyperspectral band selection based on deep reinforcement learning (DRL) ...

Classification of Hyperspectral and LiDAR Data Using Coupled CNNs

... coupled convolutional neural networks (CNNs). One CNN is designed to learn spectral-spatial features from hyperspectral data, and the other ...

Classification of hyperspectral image based on dual-branch feature ...

Hyperspectral image (HSI) classification method based on convolutional neural network (CNN) has been widely used and has achieved remarkable results.

(PDF) Dual Graph Convolutional Network for Hyperspectral Image ...

classiﬁcation performance compared to state-of-the-art methods,. which shows the potential of GCN for HSI classiﬁcation. Index Terms—Classiﬁcati ...

Classification of hyperspectral image based on dual-branch feature ...

Hyperspectral image (HSI) classification method based on convolutional neural network (CNN) has been widely used and has achieved remarkable results. However, ...

DCG-Net: Enhanced Hyperspectral Image Classification with Dual ...

We propose DCG-Net, an innovative classification network integrating CNN and GCN architectures. Our approach includes the development of a double-branch ...

Dual interactive graph convolutional networks for hyperspectral ...

Adaptation models · Convolution · Deep learning · graph convolutional network (GCN) · graph refinement · hyperspectral image (HSI) classification.

Using dual-channel CNN to classify hyperspectral image based on ...

A dual-channel CNN model has been proposed to boost its discriminative capability for HSI classification.

lironui/Double-Branch-Dual-Attention-Mechanism-Network - GitHub

This repository implementates 6 frameworks for hyperspectral image classification based on PyTorch and sklearn. The detailed results can be seen in the ...

Graph Convolutional Networks for Hyperspectral Image Classification

3) Last but not least, a trained GCN-based model fails to predict the new input samples (i.e., out of samples) without retraining the network, which has a major ...

Attention based Dual-Branch Complex Feature Fusion Network for ...

This research work presents a novel dual-branch model for hyperspectral image classification that combines two streams.

Classification of hyperspectral image based on dual-branch feature ...

A dual-branch feature interaction (DBFI) network based on CNN and ViT is proposed, which allows global information and local feature to fully interact to ...

Using dual-channel CNN to classify hyperspectral image based on ...

The proposed dual-channel CNN model has several distinct advantages. Firstly, the model consists of spectral feature extraction channel and ...