Spectral-Spatial Global Graph Reasoning for Hyperspectral Image Classification (2021-06-26T00:00:00.000000Z)

TL;DR

This article performs the superpixel generation on intermediate features during network training to adaptively produce homogeneous regions, obtain graph structures, and further generate spatial descriptors, which are served as graph nodes, to obtain a spectral-spatial graph reasoning network (SSGRN).

Abstract

Convolutional neural networks (CNNs) have been widely applied to hyperspectral image classification (HSIC). However, traditional convolutions can not effectively extract features for objects with irregular distributions. Recent methods attempt to address this issue by performing graph convolutions on spatial topologies, but fixed graph structures and local perceptions limit their performances. To tackle these problems, in this article, different from previous approaches, we perform the superpixel generation on intermediate features during network training to adaptively produce homogeneous regions, obtain graph structures, and further generate spatial descriptors, which are served as graph nodes. Besides spatial objects, we also explore the graph relationships between channels by reasonably aggregating channels to generate spectral descriptors. The adjacent matrices in these graph convolutions are obtained by considering the relationships among all descriptors to realize global perceptions. By combining the extracted spatial and spectral graph features, we finally obtain a spectral-spatial graph reasoning network (SSGRN). The spatial and spectral parts of SSGRN are separately called spatial and spectral graph reasoning subnetworks. Comprehensive experiments on four public datasets demonstrate the competitiveness of the proposed methods compared with other state-of-the-art graph convolution-based approaches.

Authors

Bo Du

5 papers

L. Zhang

5 papers

Di Wang

1 papers

TL;DR

Abstract

Authors

References67 items

Semi-Supervised Multiscale Dynamic Graph Convolution Network for Hyperspectral Image Classification

Semisupervised Cross-Scale Graph Prototypical Network for Hyperspectral Image Classification

Generating 2m fine-scale urban tree cover product over 34 metropolises in China based on deep context-aware sub-pixel mapping network

Spatial–Spectral Unified Adaptive Probability Graph Convolutional Networks for Hyperspectral Image Classification

A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection

Dual Interactive Graph Convolutional Networks for Hyperspectral Image Classification

Dual Graph Convolutional Network for Hyperspectral Image Classification With Limited Training Samples

Adaptive Spectral–Spatial Multiscale Contextual Feature Extraction for Hyperspectral Image Classification

Fully Contextual Network for Hyperspectral Scene Parsing

Hyperspectral Image Denoising Using a 3-D Attention Denoising Network

Residual Spectral-Spatial Attention Network for Hyperspectral Image Classification

CNN-Enhanced Graph Convolutional Network With Pixel- and Superpixel-Level Feature Fusion for Hyperspectral Image Classification

Beyond the Patchwise Classification: Spectral-Spatial Fully Convolutional Networks for Hyperspectral Image Classification

Graph Convolutional Networks for Hyperspectral Image Classification

Efficient Deep Learning of Nonlocal Features for Hyperspectral Image Classification

Hyperspectral Image Classification With Attention-Aided CNNs

FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification

Feature Extraction for Classification of Hyperspectral and LiDAR Data Using Patch-to-Patch CNN

Learning to Pay Attention on Spectral Domain: A Spectral Attention Module-Based Convolutional Network for Hyperspectral Image Classification

Hyperspectral Image Classification With Context-Aware Dynamic Graph Convolutional Network

Object-Contextual Representations for Semantic Segmentation

ACFNet: Attentional Class Feature Network for Semantic Segmentation

Expectation-Maximization Attention Networks for Semantic Segmentation

Multiscale Dynamic Graph Convolutional Network for Hyperspectral Image Classification

Spectral-Spatial Attention Networks for Hyperspectral Image Classification

Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images

Learning Compact and Discriminative Stacked Autoencoder for Hyperspectral Image Classification

Learning Dual Geometric Low-Rank Structure for Semisupervised Hyperspectral Image Classification

Graph-Based Global Reasoning Networks

CCNet: Criss-Cross Attention for Semantic Segmentation

Dual Attention Network for Scene Segmentation

OCNet: Object Context Network for Scene Parsing

BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation

Hyperspectral image classification via a random patches network

Superpixel Sampling Networks

Exploring Hierarchical Convolutional Features for Hyperspectral Image Classification

Spectral–Spatial Unified Networks for Hyperspectral Image Classification

Context Encoding for Semantic Segmentation

Diverse Region-Based CNN for Hyperspectral Image Classification

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

Non-local Neural Networks

Rethinking Atrous Convolution for Semantic Image Segmentation

Large Kernel Matters — Improve Semantic Segmentation by Global Convolutional Network

Estimating Soil Salinity Under Various Moisture Conditions: An Experimental Study

Pyramid Scene Parsing Network

RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation

Semi-Supervised Classification with Graph Convolutional Networks

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

Crop Classification Based on Feature Band Set Construction and Object-Oriented Approach Using Hyperspectral Images

Going Deeper With Contextual CNN for Hyperspectral Image Classification

Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach

Deep Convolutional Neural Networks for Hyperspectral Image Classification

U-Net: Convolutional Networks for Biomedical Image Segmentation

Fully convolutional networks for semantic segmentation

Classification of hyperspectral image based on deep belief networks

Deep Learning-Based Classification of Hyperspectral Data

SLIC Superpixels Compared to State-of-the-Art Superpixel Methods

A tutorial overview of anomaly detection in hyperspectral images

EMS-GCN: An End-to-End Mixhop Superpixel-Based Graph Convolutional Network for Hyperspectral Image Classification

Adaptive Sampling Toward a Dynamic Graph Convolutional Network for Hyperspectral Image Classification

Spatial Pooling Graph Convolutional Network for Hyperspectral Image Classification

Automatic Graph Learning Convolutional Networks for Hyperspectral Image Classification

Superpixel Spectral–Spatial Feature Fusion Graph Convolution Network for Hyperspectral Image Classification

Global Consistent Graph Convolutional Network for Hyperspectral Image Classification

Visualizing Data using t-SNE

Hyperspectral Image Classiﬁcation Based on 3-D Octave Convolution With Spatial–Spectral Attention Network

Hyperspectral Image Classiﬁcation Based on Deep Attention Graph Convolutional Network

Field of Study

Journal Information

Name

Page

Volume

Venue Information

Name

Type

URL