A semi-supervised convolutional neural network for hyperspectral image classification

Liu, Bing; Yu, Xi; Zhang, Pengqiang; Tan, Xiong; Yu, Anzhu; Xue, Zhixiang

doi:10.1080/2150704x.2017.1331053

Cited by 179 publications

(80 citation statements)

References 17 publications

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“…In order to verify the effectiveness of the proposed method in HSI few-shot classification, we compared the experimental results of RN-FSC with the widely used SVM, two classical semisupervised methods LapSVM and TSVM provided in [53], the deep learning model Res-3D-CNN [54], two semisupervised deep models SS-CNN [35] and DCGAN+SEMI [55], and the graph convolutional network (GCN) [56] model. SVM can map nonlinear data to linearly separable high-dimensional feature spaces utilizing the kernel method, so it can obtain a better classification effect than other traditional classifiers when processing high-dimensional HSI.…”

Section: Comparison and Analysismentioning

confidence: 99%

“…In order to improve classification accuracy under the condition of limited labeled samples, semisupervised learning and data augmentation are widely applied. In [35,36], CNN was combined with semisupervised classification. In [37], Kang et al first extracted PCA, EMP, and edge-preserving features (EPF), then carried out classification by combining semisupervised method and decision confusion strategy.…”

Section: Introductionmentioning

confidence: 99%

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Deep Relation Network for Hyperspectral Image Few-Shot Classification

et al. 2020

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Deep learning has achieved great success in hyperspectral image classification. However, when processing new hyperspectral images, the existing deep learning models must be retrained from scratch with sufficient samples, which is inefficient and undesirable in practical tasks. This paper aims to explore how to accurately classify new hyperspectral images with only a few labeled samples, i.e., the hyperspectral images few-shot classification. Specifically, we design a new deep classification model based on relational network and train it with the idea of meta-learning. Firstly, the feature learning module and the relation learning module of the model can make full use of the spatial–spectral information in hyperspectral images and carry out relation learning by comparing the similarity between samples. Secondly, the task-based learning strategy can enable the model to continuously enhance its ability to learn how to learn with a large number of tasks randomly generated from different data sets. Benefitting from the above two points, the proposed method has excellent generalization ability and can obtain satisfactory classification results with only a few labeled samples. In order to verify the performance of the proposed method, experiments were carried out on three public data sets. The results indicate that the proposed method can achieve better classification results than the traditional semisupervised support vector machine and semisupervised deep learning models.

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Section: Comparison and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Relation Network for Hyperspectral Image Few-Shot Classification

et al. 2020

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“…The spectral values of the HSIs in the third dimension are approximately continuous, and the curves of each feature possess a unique spectral plot that is different from those of other classes. In the traditional classification methods, one-dimensional spectral vectors are used as the final form of input data [27,28] or neighboring pixels are used to form small regional pixel blocks as input data [29,30]. Although the former simplifies the complexity of deep learning network training, it omits spatial dimension information of spectral values at the same time.…”

Section: Introductionmentioning

confidence: 99%

“…A small area pixel block was selected as the input unit. Liu et al [30] used the BN algorithm to the CNN for the HIS. However, the introducing heterogeneous noises and wasting scarce samples will weaken the BN algorithm's role in network regularization and accelerated training.…”

Section: Introductionmentioning

confidence: 99%

Convolution Neural Network Based on Two-Dimensional Spectrum for Hyperspectral Image Classification

Lin

Yang

et al. 2018

Journal of Sensors

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Inherent spectral characteristics of hyperspectral image (HSI) data are determined and need to be deeply mined. A convolution neural network (CNN) model of two-dimensional spectrum (2D spectrum) is proposed based on the advantages of deep learning to extract feature and classify HSI. First of all, the traditional data processing methods which use small area pixel block or one-dimensional spectral vector as input unit bring many heterogeneous noises. The 2D-spectrum image method is proposed to solve the problem and make full use of spectral value and spatial information. Furthermore, a batch normalization algorithm (BN) is introduced to address internal covariate shifts caused by changes in the distribution of input data and expedite the training of the network. Finally, Softmax loss models are proposed to induce competition among the outputs and improve the performance of the CNN model. The HSI datasets of experiments include Indian Pines, Salinas, Kennedy Space Center (KSC), and Botswana. Experimental results show that the overall accuracies of the 2D-spectrum CNN model can reach 98.26%, 97.28%, 96.22%, and 93.64%. These results are higher than the accuracies of other traditional methods described in this paper. The proposed model can achieve high target classification accuracy and efficiency.

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“…For example, a deep active learning method and a semisupervised CNN were constructed [23–25]. The experimental results demonstrated the effectiveness of these methods for hyperspectral or optical image recognition.…”

Section: Introductionmentioning

confidence: 99%

A Novel Active Semisupervised Convolutional Neural Network Algorithm for SAR Image Recognition

Gao

Yue

Wang

et al. 2017

Computational Intelligence and Neuroscience

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Convolutional neural network (CNN) can be applied in synthetic aperture radar (SAR) object recognition for achieving good performance. However, it requires a large number of the labelled samples in its training phase, and therefore its performance could decrease dramatically when the labelled samples are insufficient. To solve this problem, in this paper, we present a novel active semisupervised CNN algorithm. First, the active learning is used to query the most informative and reliable samples in the unlabelled samples to extend the initial training dataset. Next, a semisupervised method is developed by adding a new regularization term into the loss function of CNN. As a result, the class probability information contained in the unlabelled samples can be maximally utilized. The experimental results on the MSTAR database demonstrate the effectiveness of the proposed algorithm despite the lack of the initial labelled samples.

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A semi-supervised convolutional neural network for hyperspectral image classification

Cited by 179 publications

References 17 publications

Deep Relation Network for Hyperspectral Image Few-Shot Classification

Deep Relation Network for Hyperspectral Image Few-Shot Classification

Convolution Neural Network Based on Two-Dimensional Spectrum for Hyperspectral Image Classification

A Novel Active Semisupervised Convolutional Neural Network Algorithm for SAR Image Recognition

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