Tri-CNN: A Three Branch Model for Hyperspectral Image Classification

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Hyperspectral image classification method based on M-3DCNN-Attention

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Summary: This study proposes an HSI classification method based on M-3DCNN-Attention, which improves classification accuracy through the construction of virtual samples and an enhanced network structure, outperforming comparative methods.

JOURNAL OF APPLIED REMOTE SENSING (2022)

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Article Computer Science, Information Systems

Hyperspectral image classification via parallel multi-input mechanism-based convolutional neural network

Huan Zhong et al.

Summary: In this paper, a parallel multi-input mechanism-based CNN model (PMI-CNN) is proposed to fully exploit the spectral-spatial information in Hyperspectral Images. Experimental results show that PMI-CNN outperforms other models in terms of classification performance.

MULTIMEDIA TOOLS AND APPLICATIONS (2022)

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Article Environmental Sciences

Manifold-Based Multi-Deep Belief Network for Feature Extraction of Hyperspectral Image

Zhengying Li et al.

Summary: This paper proposes a new deep learning method, called manifold-based multi-DBN (MMDBN), to obtain deep manifold features of hyperspectral imagery (HSI). Compared to the original DBN model, MMDBN is able to extract deep features of different classes in HSI more effectively and maximize the margins between different manifolds in low-dimensional embedding space.

REMOTE SENSING (2022)

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Article Environmental Sciences

Consolidated Convolutional Neural Network for Hyperspectral Image Classification

Yang-Lang Chang et al.

Summary: The performance of hyperspectral image classification is influenced by spatial and spectral information, but affected by factors like data redundancy and insufficient spatial resolution. While 3D-CNN-based methods perform well, they come with high computational complexity. To address these issues, this study proposes a consolidated C-CNN method that combines 3D-CNN and 2D-CNN. PCA is used to reduce spectral redundancy, and image augmentation techniques are employed to increase training samples and prevent overfitting. The proposed C-CNN model, named C-CNN-Aug, achieves optimal trade-off between accuracy and computational time compared to other methods.

REMOTE SENSING (2022)

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Article Environmental Sciences

Attention Mechanism and Depthwise Separable Convolution Aided 3DCNN for Hyperspectral Remote Sensing Image Classification

Wenmei Li et al.

Summary: This article introduces a solution to the classification of hyperspectral remote sensing images by introducing an attention mechanism and depthwise separable convolution to a three-dimensional convolutional neural network. The proposed models, 3DCNN-AM and 3DCNN-AM-DSC, have been shown to improve classification accuracy and reduce computing time.

REMOTE SENSING (2022)

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Article Environmental Sciences

Generative Adversarial Networks Based on Transformer Encoder and Convolution Block for Hyperspectral Image Classification

Jing Bai et al.

Summary: This paper proposes a new method to address the few-shot learning problem in hyperspectral image classification. By combining a Generative Adversarial Network, Transformer Encoder, and convolution block, the proposed method achieves improved classification accuracy without the need for an external dataset.

REMOTE SENSING (2022)

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Article Computer Science, Information Systems

Small Sample Hyperspectral Image Classification Method Based on Dual-Channel Spectral Enhancement Network

Songwei Pei et al.

Summary: This paper proposes a simple yet effective CNN-based Dual-channel Spectral Enhancement Network (DSEN) for HSI classification, which fully exploits the features of small labeled HSI samples through feature fusion. Experimental results show that DSEN achieves better performance in HSI classification when dealing with small sample sizes.

ELECTRONICS (2022)

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Article Computer Science, Artificial Intelligence

Robust Self-Ensembling Network for Hyperspectral Image Classification

Yonghao Xu et al.

Summary: Recent research has shown the potential of deep learning algorithms in HSI classification, but training requires labeled data that is difficult to collect. This study proposes a robust self-ensembling network (RSEN) that can perform well in small sample situations. The RSEN consists of a base network and an ensemble network that learn from each other, utilizing both supervised and unsupervised losses. Experiments demonstrate that the proposed algorithm is competitive with state-of-the-art methods.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022)

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Article Engineering, Electrical & Electronic

Hyperspectral Image Classification Using a Hybrid 3D-2D Convolutional Neural Networks

Saeed Ghaderizadeh et al.

Summary: The study introduces the application of 3-D-CNN in hyperspectral image classification, discusses the current issues and solutions, and proposes a hybrid CNN method to address feature extraction and model complexity problems.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2021)

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Proceedings Paper Engineering, Electrical & Electronic

Hyperspectral Remote Sensing Images Classification Using Fully Convolutional Neural Network

Nyan Linn Tun et al.

Summary: In this paper, a fully convolutional neural network model for hyperspectral image classification is proposed, achieving high accuracy by learning feature maps based on the spectral-spatial information of HSI data.

PROCEEDINGS OF THE 2021 IEEE CONFERENCE OF RUSSIAN YOUNG RESEARCHERS IN ELECTRICAL AND ELECTRONIC ENGINEERING (ELCONRUS) (2021)

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Spatial-spectral hyperspectral image classification based on information measurement and CNN

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING (2020)

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HybridSN: Exploring 3-D-2-D CNN Feature Hierarchy for Hyperspectral Image Classification

Swalpa Kumar Roy et al.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2020)

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Deep Feature Fusion via Two-Stream Convolutional Neural Network for Hyperspectral Image Classification

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Deep Collaborative Attention Network for Hyperspectral Image Classification by Combining 2-D CNN and 3-D CNN

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2020)

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Unsupervised Feature-Learning for Hyperspectral Data with Autoencoders

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Hyperspectral Classification Based on Lightweight 3-D-CNN With Transfer Learning

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Deep Learning for Hyperspectral Image Classification: An Overview

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Tri-CNN: A Three Branch Model for Hyperspectral Image Classification

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