4.6 Article

Multibranch 3D-Dense Attention Network for Hyperspectral Image Classification

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Engineering, Multidisciplinary

Deep hybrid: Multi-graph neural network collaboration for hyperspectral image classification

Ding Yao et al.

Summary: In this study, a deep hybrid multi-graph neural network (DHMG) is proposed for hyperspectral image classification. Two different graph filters are utilized to extract spectral features and suppress graph noise, and a GraphSAGE-based network is introduced to refine the graph features produced by the deep hybrid network. Extensive experiments demonstrate that the DHMG model outperforms the state-of-the-art models on three public hyperspectral datasets.

DEFENCE TECHNOLOGY (2023)

Article Geochemistry & Geophysics

Graph Sample and Aggregate-Attention Network for Hyperspectral Image Classification

Yao Ding et al.

Summary: SAGE-A uses a multi-level graph sample and aggregate (graphSAGE) network to flexibly aggregate new neighbor nodes among arbitrarily structured non-Euclidean data and capture long-range contextual relations. The network utilizes the graph attention mechanism to characterize the importance among spatially neighboring regions, allowing for automatic learning of deep contextual and global information of the graph.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2022)

Article Geochemistry & Geophysics

SpectralSpatial Feature Tokenization Transformer for Hyperspectral Image Classification

Le Sun et al.

Summary: In this article, the spectral-spatial feature tokenization transformer (SSFTT) method is proposed to capture spectral-spatial and high-level semantic features. Experimental analysis confirms that this method outperforms other deep learning methods in terms of computation time and classification performance.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

Article Geochemistry & Geophysics

Semi-Supervised Locality Preserving Dense Graph Neural Network With ARMA Filters and Context-Aware Learning for Hyperspectral Image Classification

Yao Ding et al.

Summary: A novel dense graph neural network structure incorporating ARMA filters, dense structure, and context-aware learning mechanism has been proposed and applied successfully to hyperspectral image classification. Experimental results demonstrated its superiority over current state-of-the-art methods.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

Article Computer Science, Interdisciplinary Applications

Hyperspectral image classification based on optimized convolutional neural networks with 3D stacked blocks

Xiaoxia Zhang et al.

Summary: In this study, a 3D CNN network based on stacked blocks was proposed for HSI classification. The proposed network includes an attention mechanism to filter out interfering information. The optimized architecture achieved higher classification rates compared to related works and demonstrated effectiveness and adaptability on a more complex dataset.

EARTH SCIENCE INFORMATICS (2022)

Article Computer Science, Information Systems

Spatial Attention Guided Residual Attention Network for Hyperspectral Image Classification

Ningyang Li et al.

Summary: This article introduces a SpaAG-RAN model for HSI classification, which extracts discriminating spectral-spatial features through spatial attention guided residual attention network and achieves better performance than existing methods.

IEEE ACCESS (2022)

Article Environmental Sciences

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

Kun Sun et al.

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)

Article Computer Science, Information Systems

Cyclic learning rate based HybridSN model for hyperspectral image classification

Pranshu Prakash Vaish et al.

Summary: The classification of remotely sensed hyperspectral images is challenging due to the large number of spectral bands and limited data. This paper proposes a HybridSN model and uses cyclic learning to improve the test performance. The introduction of a new cyclic function further enhances the accuracy of the proposed model.

MULTIMEDIA TOOLS AND APPLICATIONS (2022)

Article Computer Science, Information Systems

AF2GNN: Graph convolution with adaptive filters and aggregator fusion for hyperspectral image classification

Yao Ding et al.

Summary: In this paper, a graph neural network model suitable for hyperspectral image classification is proposed. By the fusion mechanism of adaptive filters and aggregators, the problems of land cover discrimination, noise impaction, and spatial feature learning are addressed. Experimental results show that the proposed method outperforms existing methods.

INFORMATION SCIENCES (2022)

Article Engineering, Electrical & Electronic

Hyperspectral image classification using an extended Auto-Encoder method

Elham Kordi Ghasrodashti et al.

Summary: This article proposes a spectral-spatial method for classification of hyperspectral images by modifying traditional Auto-Encoder based on Majorization Minimization technique. The proposed method suggests three main modifications to improve classification accuracy, including using SAM criterion for constructing weights, fuzzy mode for estimating parameters, and extracting multi-scale features. Experimental results show significant improvement in HSI classification accuracy compared to state-of-the-art methods.

SIGNAL PROCESSING-IMAGE COMMUNICATION (2021)

Article Geochemistry & Geophysics

Cooperative Spectral-Spatial Attention Dense Network for Hyperspectral Image Classification

Zhimin Dong et al.

Summary: This letter proposes a cooperative spectral-spatial attention dense network (CS(2)ADN) for hyperspectral image classification. By employing attention modules and dense connections, the method achieves better classification performance with lower computational cost.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2021)

Article Engineering, Electrical & Electronic

Multiscale Graph Sample and Aggregate Network With Context-Aware Learning for Hyperspectral Image Classification

Yao Ding et al.

Summary: In this article, a multiscale graph sample and aggregate network with a context-aware learning method is proposed for HSI classification. This network can learn global and contextual information of the graph effectively, and solve the impact of original input graph errors on classification. Experimental results show the superiority of the proposed method over state-of-the-art methods.

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

Article Geochemistry & Geophysics

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)

Article Engineering, Electrical & Electronic

Local Binary Patterns and Superpixel-Based Multiple Kernels for Hyperspectral Image Classification

Wei Huang et al.

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

Article Computer Science, Artificial Intelligence

Hyperspectral and Multispectral Image Fusion Using Optimized Twin Dictionaries

Xiaolin Han et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2020)

Article Engineering, Electrical & Electronic

A Novel Cubic Convolutional Neural Network for Hyperspectral Image Classification

Jinwei Wang et al.

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

Article Environmental Sciences

Spectral-Spatial Attention Networks for Hyperspectral Image Classification

Xiaoguang Mei et al.

REMOTE SENSING (2019)

Article Geochemistry & Geophysics

Multi-Scale Dense Networks for Hyperspectral Remote Sensing Image Classification

Chunju Zhang et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2019)

Article Computer Science, Information Systems

Adaptive Spatial-Spectral Feature Learning for Hyperspectral Image Classification

Simin Li et al.

IEEE ACCESS (2019)

Article Automation & Control Systems

Hyperspectral Mineral Target Detection Based on Density Peak

Yani Hou et al.

INTELLIGENT AUTOMATION AND SOFT COMPUTING (2019)

Article Engineering, Electrical & Electronic

Spatial Sequential Recurrent Neural Network for Hyperspectral Image Classification

Xiangrong Zhang et al.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2018)

Article Engineering, Electrical & Electronic

L1-Norm Distance Linear Discriminant Analysis Based on an Effective Iterative Algorithm

Qiaolin Ye et al.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (2018)

Article Geochemistry & Geophysics

Spectral-Spatial Unified Networks for Hyperspectral Image Classification

Yonghao Xu et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2018)

Article Geochemistry & Geophysics

Spectral-Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework

Zilong Zhong et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2018)

Article Computer Science, Information Systems

Fusion of Weighted Mean Reconstruction and SVMCK for Hyperspectral Image Classification

Hong Huang et al.

IEEE ACCESS (2018)

Review Geochemistry & Geophysics

Multiple Kernel Learning for Hyperspectral Image Classification: A Review

Yanfeng Gu et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2017)

Article Geochemistry & Geophysics

Deep Recurrent Neural Networks for Hyperspectral Image Classification

Lichao Mou et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2017)

Article Geochemistry & Geophysics

Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks

Yushi Chen et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2016)

Article Geochemistry & Geophysics

A Survey on Spectral-Spatial Classification Techniques Based on Attribute Profiles

Pedram Ghamisi et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2015)

Article Remote Sensing

On combining multiscale deep learning features for the classification of hyperspectral remote sensing imagery

Wenzhi Zhao et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2015)

Article Engineering, Electrical & Electronic

Deep Convolutional Neural Networks for Hyperspectral Image Classification

Wei Hu et al.

JOURNAL OF SENSORS (2015)

Article Engineering, Electrical & Electronic

Advances in Hyperspectral Image Classification

Gustavo Camps-Valls et al.

IEEE SIGNAL PROCESSING MAGAZINE (2014)

Article Geochemistry & Geophysics

Hyperspectral Remote Sensing Data Analysis and Future Challenges

Jose M. Bioucas-Dias et al.

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE (2013)

Article Geochemistry & Geophysics

Gaussian Process Approach to Remote Sensing Image Classification

Yakoub Bazi et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2010)

Review Remote Sensing

A review on hyperspectral remote sensing for homogeneous and heterogeneous forest biodiversity assessment

Azadeh Ghiyamat et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2010)

Article Environmental Sciences

Ash decline assessment in emerald ash borer-infested regions: A test of tree-level, hyperspectral technologies

Jennifer Pontius et al.

REMOTE SENSING OF ENVIRONMENT (2008)

Article Geochemistry & Geophysics

Classification of hyperspectral remote sensing images with support vector machines

F Melgani et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2004)