4.7 Article

Local aggregation and global attention network for hyperspectral image classification with spectral-induced aligned superpixel segmentation

Related references

Note: Only part of the references are listed.
Article Geochemistry & Geophysics

Center Weighted Convolution and GraphSAGE Cooperative Network for Hyperspectral Image Classification

Ying Cui et al.

Summary: This paper introduces the basic task of hyperspectral image (HSI) classification and the recent research progress of convolutional neural network (CNN) and graph convolution neural network (GCN) in this field. The authors propose a cooperative network that combines CNN and GraphSAGE, and extract features through superpixels and CNN with center attention. The advantages of this method are validated through experiments.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2023)

Article Geochemistry & Geophysics

A Fast and Compact 3-D CNN for Hyperspectral Image Classification

Muhammad Ahmad et al.

Summary: This study proposes a 3-D CNN model that utilizes both spatial-spectral feature maps to improve the performance of HSIC. By processing small overlapping 3-D patches and generating 3-D feature maps, the model demonstrates remarkable performance in terms of accuracy and computational time.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2022)

Article Computer Science, Artificial Intelligence

Multi-feature fusion: Graph neural network and CNN combining for hyperspectral image classification

Yao Ding et al.

Summary: This paper proposes a novel multi-feature fusion network (MFGCN) for hyperspectral image (HSI) classification. It utilizes multi-scale GCN and multi-scale CNN to refine and extract pixel-wise spectral-spatial features of HSI, and introduces a 1D CNN to extract spectral features for superpixels. The complementary multi-scale features are fused through concatenate operation. Experimental results show that the proposed method outperforms competitive methods on three datasets.

NEUROCOMPUTING (2022)

Article Geochemistry & Geophysics

Hybrid Deep-Learning Network for Rapid On-Site Peak Ground Velocity Prediction

Jingbao Zhu et al.

Summary: Accurately predicting on-site peak ground velocity (PGV) is crucial for earthquake hazard mitigation. In this study, a hybrid deep-learning network (HybridNet) is constructed to predict PGV using a combination of CNN and RNN feature extraction blocks. The HybridNet model shows better performance than baseline models in terms of error deviation, mean absolute error, and coefficient of determination for PGV prediction. Additionally, the predicted PGV can be used to estimate the potential damage zone (PDZ) by interpolating values at different stations, which aligns well with observed results shortly after the arrival of P-wave.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

Article Geochemistry & Geophysics

Nonoverflow Representation of Electromagnetic Field From Dipole Source in Cylindrical Media With Uniaxial Anisotropy

Decheng Hong

Summary: This paper presents a nonoverflow representation of the electromagnetic field radiated by dipole sources in anisotropic media, utilizing normalized reflection/transmission coefficients to describe wave propagation and a recursive algorithm for field calculation in each layer. The study discusses the parity of integrands through numerical simulations to accelerate computational efficiency.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

Article Remote Sensing

Adaptive spectral-spatial feature fusion network for hyperspectral image classification using limited training samples

Hongmin Gao et al.

Summary: This article investigates the limitations of current CNN-based methods for extracting and utilizing HSI features. A novel spectral band non-localization operation is proposed to excavate inter-band correlations, and a multiscale-share Inception block is developed to exploit cross-relationships among spatial multiscale features. Additionally, an adaptive feature fusion module is introduced to better utilize the complementary information of spectral and spatial features.

INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION (2022)

Article Geochemistry & Geophysics

Bispace Domain Adaptation Network for Remotely Sensed Semantic Segmentation

Wei Liu et al.

Summary: This article proposes a bispace alignment network (BSANet) for supervised learning in domain adaptation, which is capable of extracting features in both the image domain and the wavelet domain simultaneously. By introducing a bispace adversarial learning strategy, the proposed method achieves training of an end-to-end semantic segmentation network without using any labels in the target domain.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

Article Geochemistry & Geophysics

Graph Convolutional Networks for Hyperspectral Image Classification

Danfeng Hong et al.

Summary: This article thoroughly investigates the applications of Convolutional Neural Networks (CNNs) and Graph Convolutional Networks (GCNs) in hyperspectral image classification. By developing a new minibatch GCN (miniGCN) to train and infer large-scale GCNs, as well as exploring fusion strategies for different types of HS features, the study achieves good classification performance.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

Proceedings Paper Computer Science, Artificial Intelligence

REVISITING GRAPH CONVOLUTIONAL NETWORKS WITH MINI-BATCH SAMPLING FOR HYPERSPECTRAL IMAGE CLASSIFICATION

Danfeng Hong et al.

Summary: Graph convolutional networks have been widely used in computer vision and machine learning, but traditional GCNs have limitations in extracting features for large-scale graphs and considering all samples simultaneously. This paper introduces a novel mini-batch GCN for hyperspectral image classification, which can effectively train the network and infer new samples without re-training. Experimental results show the superiority of miniGCN over other state-of-the-art network architectures in two commonly-used HSI datasets.

2021 11TH WORKSHOP ON HYPERSPECTRAL IMAGING AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING (WHISPERS) (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 Geochemistry & Geophysics

Multiscale Dynamic Graph Convolutional Network for Hyperspectral Image Classification

Sheng Wan et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2020)

Article Geochemistry & Geophysics

Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification

Danfeng Hong et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2020)

Article Geochemistry & Geophysics

Spectral-Spatial Graph Convolutional Networks for Semisupervised Hyperspectral Image Classification

Anyong Qin et al.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2019)

Article Geochemistry & Geophysics

Deep Pyramidal Residual Networks for Spectral-Spatial Hyperspectral Image Classification

Mercedes E. Paoletti et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2019)

Article Geochemistry & Geophysics

A CNN With Multiscale Convolution and Diversified Metric for Hyperspectral Image Classification

Zhiqiang Gong et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2019)

Article Geochemistry & Geophysics

Deep Learning for Hyperspectral Image Classification: An Overview

Shutao Li et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2019)

Proceedings Paper Computer Science, Artificial Intelligence

DeepGCNs: Can GCNs Go as Deep as CNNs?

Guohao Li et al.

2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019) (2019)

Article Engineering, Electrical & Electronic

Classification of VHR Multispectral Images Using ExtraTrees and Maximally Stable Extremal Region-Guided Morphological Profile

Alim Samat et al.

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

Article Computer Science, Artificial Intelligence

Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network

Xiangyong Cao et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2018)

Article Environmental Sciences

Convolutional Recurrent Neural Networks for Hyperspectral Data Classification

Hao Wu et al.

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 Geochemistry & Geophysics

Hyperspectral Image Classification Using Dictionary-Based Sparse Representation

Yi Chen et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2011)

Article Geochemistry & Geophysics

Morphological Attribute Profiles for the Analysis of Very High Resolution Images

Mauro Dalla Mura et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2010)

Article Remote Sensing

A pairwise decision tree framework for hyperspectral classification

J. Chen et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2007)

Article Geochemistry & Geophysics

Classification of hyperspectral data from urban areas based on extended morphological profiles

JA Benediktsson et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2005)

Article Geochemistry & Geophysics

Investigation of the random forest framework for classification of hyperspectral data

J Ham et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2005)

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)