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

Liu, Qichao; Xiao, Liang; Yang, Jingxiang; Wei, Zhihui

doi:10.1109/tgrs.2020.3037361

Cited by 231 publications

(89 citation statements)

References 46 publications

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“…A novel idea of deep attention GCN is introduced in [ 222 ] based on similarity measurement criteria between the mixed measurement of a kernel-spectral angle mapper and spectral information divergence to accumulate analogous spectra. [ 223 ] emerges as a collaboration between CNN and GCN to extract pixel and super-pixelwise joint features by learning small-scale regular regions and large-scale irregular regions.…”

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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“…In future work, we will focus on exploring a more concise network structure while enhancing the classification accuracy of various datasets. Since the graph convolutional network can perform flexible convolution on arbitrary irregular image regions, it has received extensive attention, which is also one of our main research works in the future [46,47]. In addition, due to the limited number of training samples, obtaining labeled sample data is usually very time-consuming, we will also explore semi-supervised or unsupervised deep learning methods for HSI classification.…”

Section: Discussionmentioning

confidence: 99%

Multiscale DenseNet Meets With Bi-RNN for Hyperspectral Image Classification

Liang

Zhang

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Convolutional neural network (CNN) has been successfully introduced to hyperspectral image (HSI) classification and achieved effective performance. With the depth of the CNN increases, it may cause the gradient to become zero, and the structure lacks the utilization of the correlated spatial feature information between different convolutional layers. At the same time, this single-scale convolution kernel is insufficient in expressing the complex spatial structure information of HSI. Additionally, the CNN-based methods treat the HSIs spectral band data as a disordered vector in the process of feature extraction, which abandons the exploitation of its internal spectral correlations. To address these issues, we propose a novel spectral-spatial network classification framework based on multiscale dense connected convolutional network (DenseNet) and bi-direction recurrent neural network (Bi-RNN) with attention mechanism network (MDRN). For the proposed MDRN, in terms of spatial feature extraction, a multi-scale DenseNet is exploited to combine shallow and deep convolution features to extract the multi-scale and complex spatial structure features at each layer. In the aspects of spectral feature extraction, Bi-RNN with attention mechanism is used to capture the inner spectral correlations within a continuous spectrum. Three standard real hyperspectral datasets were used to verify the effectiveness of the proposed MDRN approach. Experimental results indicate that the proposed MDRN method can make full use of the spectral and spatial information of the image, and it has better performance than some advanced algorithms in HSI classification. Finally, in the application of hyperspectral data captured by Gaofen-5 (GF-5) satellite, the practicability of the proposed MDRN method is also superior to other methods.

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“…In order to validate the performance of our proposed framework MSGLAMS, seven state-of-the-art algorithms are selected for comparison. These methods include three HSI classification methods based on SP segmentation: CEGCN [37], STSE_DWLR [36], and SGL [38]. Two HSI classification methods to solve small sample problems: KDCDWBF [20] and 3D-CNN [19].…”

Section: Experimental Settings For Comparing With Other State Of the ...mentioning

confidence: 99%

“…Then, Zheng et al [36] proposed an SP-guided training sample enlargement and the distance weighted linear regression-based classification (STSE_DWLR) to solve STLSS problem. Liu et al [37] applied SP segmentation to deep learning and employed the graph convolutional networks (GCN) to extract global features. They proposed the CNN-enhanced GCN (CEGCN) to integrate the complementary advantages of CNN and GCN, and achieved the pixel-and superpixellevel feature fusion.…”

Section: Introductionmentioning

confidence: 99%

Multiple Superpixel Graphs Learning Based on Adaptive Multiscale Segmentation for Hyperspectral Image Classification

et al. 2022

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Hyperspectral image classification (HSIC) methods usually require more training samples for better classification performance. However, a large number of labeled samples are difficult to obtain because it is cost- and time-consuming to label an HSI in a pixel-wise way. Therefore, how to overcome the problem of insufficient accuracy and stability under the condition of small labeled training sample size (SLTSS) is still a challenge for HSIC. In this paper, we proposed a novel multiple superpixel graphs learning method based on adaptive multiscale segmentation (MSGLAMS) for HSI classification to address this problem. First, the multiscale-superpixel-based framework can reduce the adverse effect of improper selection of a superpixel segmentation scale on the classification accuracy while saving the cost to manually seek a suitable segmentation scale. To make full use of the superpixel-level spatial information of different segmentation scales, a novel two-steps multiscale selection strategy is designed to adaptively select a group of complementary scales (multiscale). To fix the bias and instability of a single model, multiple superpixel-based graphical models obatined by constructing superpixel contracted graph of fusion scales are developed to jointly predict the final results via a pixel-level fusion strategy. Experimental results show that the proposed MSGLAMS has better performance when compared with other state-of-the-art algorithms. Specifically, its overall accuracy achieves 94.312%, 99.217% ,98.373% and 92.693% on Indian Pines, Salinas and University of Pavia, and the more challenging dataset Houston2013, respectively.

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

Cited by 231 publications

References 46 publications

Hyperspectral Image Classification: Potentials, Challenges, and Future Directions

Hyperspectral Image Classification: Potentials, Challenges, and Future Directions

Multiscale DenseNet Meets With Bi-RNN for Hyperspectral Image Classification

Multiple Superpixel Graphs Learning Based on Adaptive Multiscale Segmentation for Hyperspectral Image Classification

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