Deep Multiview Learning for Hyperspectral Image Classification

Liu, Bing; Yu, Anzhu; Yu, Xi; Wang, Ruirui; Gao, Kuiliang; Guo, Wenyue

doi:10.1109/tgrs.2020.3034133

Cited by 100 publications

(32 citation statements)

References 46 publications

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“…In addition, Dense Network (DenseNet) (Bai et al, 2019), Capsule Network (CN) (Jihao et al, 2019), Siamese Network (SN) (Bing and other novel network structures achieved better classification accuracy with sufficient labeled samples. However, the training process of deep learning method needs a large number of labeled samples, obtaining high-quality samples is a timeconsuming and laborious work (Liu et al, 2020). For this reason, how to improve the classification accuracy of hyperspectral images under the conditions of small samples has become a research hotspot.…”

Section: Introductionmentioning

confidence: 99%

Graph inductive learning method for small sample classification of hyperspectral remote sensing images

Zuo

Liu

et al. 2020

European Journal of Remote Sensing

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In recent years, deep learning has drawn increasing attention in the field of hyperspectral remote sensing image classification and has achieved great success. However, the traditional convolutional neural network model has a huge parameter space, in order to obtain a better classification model, a large number of labeled samples are often required. In this paper, a graph induction learning method is proposed to solve the problem of small sample in hyperspectral image classification. It treats each pixel of the hyperspectral image as a graph node and learns the aggregation function of adjacent vertices through graph sampling and graph aggregation operations to generate the embedding vector of the target vertex. Experimental results on three well-known hyperspectral data sets show that this method is superior to the current semi-supervised methods and advanced deep learning methods.

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Section: Introductionmentioning

confidence: 99%

Graph inductive learning method for small sample classification of hyperspectral remote sensing images

Zuo

Liu

et al. 2020

European Journal of Remote Sensing

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“…Recently, computer vision has become a mainstream research direction and has been applied in many fields, such as agronomy [11,12], plant science [13], remote sensing and so on. After deep learning (DL) is applied to HSI classification, the detailed spectral-spatial features can be extracted [14][15][16][17]. The authors in [18] proposed a 1D+2D HSIC method, which uses a one-dimensional (1D) convolution kernel to extract spectral features and a two-dimensional (2D) convolution kernel to extract spatial features.…”

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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“…Aiming to explore the intrinsic complexity of HSI, the deep pyramidal residual networks use pyramidal bottleneck residual blocks to learn high-level spectral-spatial features [19]. To solve the small samples classification of HSI, deep few-shot learning and multiview learning are proposed in the deep residual learning framework recently [20], [21]. Dense network (DenseNet) connects each layer to every other layer in a feed-forward way, which can also alleviate the vanishing-gradient problem [22].…”

Section: Introductionmentioning

confidence: 99%

HResNetAM: Hierarchical Residual Network With Attention Mechanism for Hyperspectral Image Classification

Xue

Liu

et al. 2021

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

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This article proposes a novel hierarchical residual network with attention mechanism (HResNetAM) for hyperspectral image spectral-spatial classification to improve the performance of conventional deep learning networks. The straightforward convolutional neural network based models have limitations in exploiting the multi-scale spatial and spectral features, and this is the key factor in dealing with the high-dimensional nonlinear characteristics present in hyperspectral images. The proposed hierarchical residual network can extract multi-scale spatial and spectral features at a granular level, so the receptive fields range of this network will be increased, which can enhance the feature representation ability of the model. Besides, we utilize the attention mechanism to set adaptive weights for spatial and spectral features of different scales, and this can further improve the discriminative ability of extracted features. Furthermore, the double branch structure is also exploited to extract spectral and spatial features with corresponding convolution kernels in parallel, and the extracted spatial and spectral features of multiple scales are fused for hyperspectral image classification. Four benchmark hyperspectral datasets collected by different sensors and at different acquisition time are employed for classification experiments, and comparative results reveal that the proposed method has competitive advantages in terms of classification performance when compared with other state-ofthe-art deep learning models.

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Deep Multiview Learning for Hyperspectral Image Classification

Cited by 100 publications

References 46 publications

Graph inductive learning method for small sample classification of hyperspectral remote sensing images

Graph inductive learning method for small sample classification of hyperspectral remote sensing images

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

HResNetAM: Hierarchical Residual Network With Attention Mechanism for Hyperspectral Image Classification

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