3D convolutional siamese network for few-shot hyperspectral classification

Cao, Zeyu; Li, Xiaorun; Jiang, Jianfeng; Zhao, Liaoying

doi:10.1117/1.jrs.14.048504

Cited by 26 publications

(22 citation statements)

References 27 publications

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“…The vectors are fed into projection head and prediction head. With reference to the setting of [42], we make the dimension of the output vector equal to 256, i.e. z ∈ R 256 , where z=h(f (x)).…”

Section: Symmetric Loss and Cross Entropy Lossmentioning

confidence: 99%

Bag-of-Features-Driven Spectral-Spatial Siamese Neural Network for Hyperspectral Image Classification

Xue

Zhu

Zhou³

et al. 2023

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

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Deep learning (DL) exhibits commendable performance in HSI classification because of its powerful feature expression ability. Siamese neural network further improves the performance of DL models by learning similarities withinclass and differences between-class from sample pairs. However, there are still some limitations in siamese neural network. On the one hand, siamese neural network usually needs large number of negative pair samples in training process, leading to computing overhead. On the other hand, current models may lack interpretability because of complex network structure. To overcome the above limitations, we propose a spectral-spatial siamese neural network with Bag-of-Features (S3BoF) for HSI classification. Firstly, we use a siamese neural network with 3D and 2D convolutions to extract the spectral-spatial features. Secondly, we introduce stop-gradient operation and prediction head structure to make the siamese neural network work without negative pair samples, thus reducing computational burden. Thirdly, a Bag-of-Features (BoF) learning module is introduced to enhance the model interpretability and feature representation. Finally, a symmetric loss and a cross entropy loss are respectively used for contrastive learning and classification. Experiments results on four common hyperspectral data sets indicated that S3BoF performs better than the other traditional and stateof-the-art deep learning HSI classification methods in terms of classification accuracy and generalization performance, with improvements in terms of OA around 1.40%-30.01%, 0.27%-

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Section: Symmetric Loss and Cross Entropy Lossmentioning

confidence: 99%

Bag-of-Features-Driven Spectral-Spatial Siamese Neural Network for Hyperspectral Image Classification

Xue

Zhu

Zhou³

et al. 2023

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

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“…Inspired by 3DCSN [19] and Res2Net [24], the MMSN is proposed for his classification with few-shot training samples. The MMSN is based on the Siamese network framework, and each subnetwork branch mainly consists of a dilatation-cosine attention module (DCAM), residual-dense hybrid multipath (RDHM) and multikernel depth feature extraction (MDFE) module, as illustrated in the flowchart in Figure 5.…”

Section: The Proposed Mmsnmentioning

confidence: 99%

“…B. Gowthama et al integrated principal component analysis (PCA)-based dimensionality reduction, the Siamese network framework and a CNN to achieve an improved HSI classification performance with a small sample [18]. Zeyu Cao proposed a Siamese network based on a 3DCNN, which combined contrastive information and label information to process small sample classification tasks [19]. Zhaohui Xue et al utilized a spectral-spatial Siamese network consisting of a 1DCNN and a 2DCNN to extract spectral-spatial features [20].…”

Section: Introductionmentioning

confidence: 99%

“…To solve the above problems, inspired by 3DCSN [19] and Res2Net, which increases the receptive field by constructing hierarchical residual-like connections [24], a multipath and multiscale Siamese network based on spatial-spectral features for few-shot hyperspectral image classification (MMSN) is proposed. The MMSN is based on the Siamese network framework, which has low dependence on sample information.…”

Section: Introductionmentioning

confidence: 99%

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A Multipath and Multiscale Siamese Network Based on Spatial-Spectral Features for Few-Shot Hyperspectral Image Classification

Yang,

Qin,

Qian

et al. 2023

Remote Sensing

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Deep learning has been demonstrated to be a powerful nonlinear modeling method with end-to-end optimization capabilities for hyperspectral Images (HSIs). However, in real classification cases, obtaining labeled samples is often time-consuming and labor-intensive, resulting in few-shot training samples. Based on this issue, a multipath and multiscale Siamese network based on spatial-spectral features for few-shot hyperspectral image classification (MMSN) is proposed. To conduct classification with few-shot training samples, a Siamese network framework with low dependence on sample information is adopted. In one subnetwork, a spatial attention module (DCAM), which combines dilated convolution and cosine similarity to comprehensively consider spatial-spectral weights, is designed first. Then, we propose a residual-dense hybrid module (RDHM), which merges three-path features, including grouped convolution-based local residual features, global residual features and global dense features. The RDHM can effectively propagate and utilize different layers of features and enhance the expression ability of these features. Finally, we construct a multikernel depth feature extraction module (MDFE) that performs multiscale convolutions with multikernel and hierarchical skip connections on the feature scales to improve the ability of the network to capture details. Extensive experimental evidence shows that the proposed MMSN method exhibits a superior performance on few-shot training samples, and its classification results are better than those of other state-of-the-art classification methods.

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“…Within the remote sensing community, semi-supervised learning has been long studied and enjoys applications in, e.g., hyperspectral image recognition and processing [19,20,21,22,23,24,25,26,27,28], multi-spectral image segmentation [29,30,31,32,33,34,35] and SAR-optical data fusion [36].…”

Section: Introductionmentioning

confidence: 99%

Self-Supervised Learning in Remote Sensing: A review

Wang

Albrecht

Braham

et al. 2022

IEEE Geosci. Remote Sens. Mag.

132

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In deep learning research, self-supervised learning (SSL) has received great attention triggering interest within both the computer vision and remote sensing communities. While there has been a big success in computer vision, most of the potential of SSL in the domain of earth observation remains locked. In this paper, we provide an introduction to, and a review of the concepts and latest developments in SSL for computer vision in the context of remote sensing. Further, we provide a preliminary benchmark of modern SSL algorithms on popular remote sensing datasets, verifying the potential of SSL in remote sensing and providing an extended study on data augmentations. Finally, we identify a list of promising directions of future research in SSL for earth observation (SSL4EO) to pave the way for fruitful interaction of both domains.

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3D convolutional siamese network for few-shot hyperspectral classification

Cited by 26 publications

References 27 publications

Bag-of-Features-Driven Spectral-Spatial Siamese Neural Network for Hyperspectral Image Classification

Bag-of-Features-Driven Spectral-Spatial Siamese Neural Network for Hyperspectral Image Classification

A Multipath and Multiscale Siamese Network Based on Spatial-Spectral Features for Few-Shot Hyperspectral Image Classification

Self-Supervised Learning in Remote Sensing: A review

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