Global Consistent Graph Convolutional Network for Hyperspectral Image Classification

Ding, Yun; Guo, Yuanyuan; Chong, Yanwen; Pan, Shaoming; Feng, Jinpeng

doi:10.1109/tim.2021.3056750

Cited by 51 publications

(16 citation statements)

References 49 publications

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“…In [ 219 ], the different unique features collected from CNN and GCN are fused additive, elementwise, and concatenated way. A new framework of globally consistent GCN is introduced in [ 220 ], which first generates a spatial-spectral local optimized graph whose global high-order neighbors obtain the enriched contextual information employing the graph topological consistent connectivity; at last, those global features determine the classes. [ 221 ] shows the concept of a dual GCN network, which works with a limited number of training samples, where first extricates all the significant features and second learns label distribution.…”

Section: Discussionmentioning

confidence: 99%

Hyperspectral Image Classification: Potentials, Challenges, and Future Directions

Datta

Mallick

Bhoi

et al. 2022

Computational Intelligence and Neuroscience

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Recent imaging science and technology discoveries have considered hyperspectral imagery and remote sensing. The current intelligent technologies, such as support vector machines, sparse representations, active learning, extreme learning machines, transfer learning, and deep learning, are typically based on the learning of the machines. These techniques enrich the processing of such three-dimensional, multiple bands, and high-resolution images with their precision and fidelity. This article presents an extensive survey depicting machine-dependent technologies’ contributions and deep learning on landcover classification based on hyperspectral images. The objective of this study is three-fold. First, after reading a large pool of Web of Science (WoS), Scopus, SCI, and SCIE-indexed and SCIE-related articles, we provide a novel approach for review work that is entirely systematic and aids in the inspiration of finding research gaps and developing embedded questions. Second, we emphasize contemporary advances in machine learning (ML) methods for identifying hyperspectral images, with a brief, organized overview and a thorough assessment of the literature involved. Finally, we draw the conclusions to assist researchers in expanding their understanding of the relationship between machine learning and hyperspectral images for future research.

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Section: Discussionmentioning

confidence: 99%

Hyperspectral Image Classification: Potentials, Challenges, and Future Directions

Datta

Mallick

Bhoi

et al. 2022

Computational Intelligence and Neuroscience

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“…Recently, GCN which is usually used to data representation in a non-Euclidean space and can flexibly preserve class boundary information, has been employed to HSI classification [60,61]. For instance, Mou et al [16] take the whole image including both labeled and unlabeled pixels as input and utilize a set of graph convolutional layers to extract features.…”

Section: Hyperspectral Image Classificationmentioning

confidence: 99%

Spectral-Spatial Offset Graph Convolutional Networks for Hyperspectral Image Classification

et al. 2021

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In hyperspectral image (HSI) classification, convolutional neural networks (CNN) have been attracting increasing attention because of their ability to represent spectral-spatial features. Nevertheless, the conventional CNN models perform convolution operation on regular-grid image regions with a fixed kernel size and as a result, they neglect the inherent relation between HSI data. In recent years, graph convolutional networks (GCN) used for data representation in a non-Euclidean space, have been successfully applied to HSI classification. However, conventional GCN methods suffer from a huge computational cost since they construct the adjacency matrix between all HSI pixels, and they ignore the local spatial context information of hyperspectral images. To alleviate these shortcomings, we propose a novel method termed spectral-spatial offset graph convolutional networks (SSOGCN). Different from the usually used GCN models that compute the adjacency matrix between all pixels, we construct an adjacency matrix only using pixels within a patch, which contains rich local spatial context information, while reducing the computation cost and memory consumption of the adjacency matrix. Moreover, to emphasize important local spatial information, an offset graph convolution module is proposed to extract more robust features and improve the classification performance. Comprehensive experiments are carried out on three representative benchmark data sets, and the experimental results effectively certify that the proposed SSOGCN method has more advantages than the recent state-of-the-art (SOTA) methods.

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“…The non-Euclidean models aim at generalizing convolution into the graph domain. These types of models are also referred to as graph convolutional networks (GCN) [20,21]. Since Euclidean data can be regarded as special cases of graph data, GCN is a more general learning paradigm than CNN, especially learning for structural information.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Graph Convolutional RVFL Network for Hyperspectral Image Classification

Zhang,

Cai,

Liu

et al. 2023

Remote Sensing

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Graph convolutional networks (GCN) have emerged as a powerful alternative tool for analyzing hyperspectral images (HSIs). Despite their impressive performance, current works strive to make GCN more sophisticated through either elaborate architecture or fancy training tricks, making them prohibitive for HSI data in practice. In this paper, we present a Graph Convolutional RVFL Network (GCRVFL), a simple but efficient GCN for hyperspectral image classification. Specifically, we generalize the classic RVFL network into the graph domain by using graph convolution operations. This not only enables RVFL to handle graph-structured data, but also avoids iterative parameter adjustment by employing an efficient closed-form solution. Unlike previous works that perform HSI classification under a transductive framework, we regard HSI classification as a graph-level classification task, which makes GCRVFL scalable to large-scale HSI data. Extensive experiments on three benchmark data sets demonstrate that the proposed GCRVFL is able to achieve competitive results with fewer trainable parameters and adjustable hyperparameters and higher computational efficiency. In particular, we show that our approach is comparable to many existing approaches, including deep CNN models (e.g., ResNet and DenseNet) and popular GCN models (e.g., SGC and APPNP).

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Global Consistent Graph Convolutional Network for Hyperspectral Image Classification

Cited by 51 publications

References 49 publications

Hyperspectral Image Classification: Potentials, Challenges, and Future Directions

Hyperspectral Image Classification: Potentials, Challenges, and Future Directions

Spectral-Spatial Offset Graph Convolutional Networks for Hyperspectral Image Classification

An Efficient Graph Convolutional RVFL Network for Hyperspectral Image Classification

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