Semisupervised Stacked Autoencoder With Cotraining for Hyperspectral Image Classification

Zhou, Shaoguang; Xue, Zhaohui; Du, Peijun

doi:10.1109/tgrs.2018.2888485

Cited by 86 publications

(32 citation statements)

References 39 publications

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“…We measure the maximum diameter of the nodule in cm and record it, and collect ultrasound images for each module according to the actual situation. It records basic information about the patient's gender, age, and pathology follow-up, excluding crossborder and no pathological tumor patients [22]. In the end, 203 nodules from 177 patients were included in this experiment.…”

Section: B Data Source Design For Thyroid Nodulesmentioning

confidence: 99%

Application of Deep Learning in the Prediction of Benign and Malignant Thyroid Nodules on Ultrasound Images

Yang

Chen

2020

IEEE Access

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In this paper, ultrasound imaging of benign and malignant thyroid nodules to predict the depth of the learning algorithm, built on circulation volume product thyroid ultrasound image neural network forecasting model. Introduced the convolutional neural network and the recurrent neural network, and combined the advantages of the convolutional neural network and the recurrent neural network, improved the prediction model, constructed the recurrent convolutional neural network prediction model and optimized the prediction model. Soc max algorithm and L2 regularization are introduced to prevent the occurrence of over-fitting. This study introduces the technology and tools required for the development of forecasting systems, the feasibility analysis of the system, demand analysis and system design and other system development preliminary work. Describes the function of the thyroid nodule prediction system and related work such as system testing. Based on the above research, thyroid ultrasound images obtained by the cooperative hospital are used as a data set, and the cyclic convolutional neural network prediction model is used to predict training and testing to the development of a thyroid nodule prediction system. The experimental results show that the prediction system has high prediction accuracy. INDEX TERMS deep learning; ultrasound imaging; thyroid nodules; benign and malignant prediction.

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Section: B Data Source Design For Thyroid Nodulesmentioning

confidence: 99%

Application of Deep Learning in the Prediction of Benign and Malignant Thyroid Nodules on Ultrasound Images

Yang

Chen

2020

IEEE Access

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“…In the last stage, the fused features are then embedded into a sparse multinomial logistic regression (SMLR) [53] model for training and prediction. We adopt the Multinomial Logistic Regression via a Variable Splitting and Augmented Lagrangian (LORSAL) algorithm to optimize the model since LORSAL [54] has yielded efficient and powerful performances for HSI classification in recent years [55][56][57][58][59][60]. In addition, LORSAL has high flexibility in conjunction with other disciplines, such as the Markov Random Field (MRF) that models spatial information; the Gaussian radial basis function (RBF) kernel that maps the input features into more separable space.…”

Section: Classification By Using Smlrmentioning

confidence: 99%

Coupled Higher-Order Tensor Factorization for Hyperspectral and LiDAR Data Fusion and Classification

et al. 2019

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Hyperspectral and light detection and ranging (LiDAR) data fusion and classification has been an active research topic, and intensive studies have been made based on mathematical morphology. However, matrix-based concatenation of morphological features may not be so distinctive, compact, and optimal for classification. In this work, we propose a novel Coupled Higher-Order Tensor Factorization (CHOTF) model for hyperspectral and LiDAR data classification. The innovative contributions of our work are that we model different features as multiple third-order tensors, and we formulate a CHOTF model to jointly factorize those tensors. Firstly, third-order tensors are built based on spectral-spatial features extracted via attribute profiles (APs). Secondly, the CHOTF model is defined to jointly factorize the multiple higher-order tensors. Then, the latent features are generated by mode-n tensor-matrix product based on the shared and unshared factors. Lastly, classification is conducted by using sparse multinomial logistic regression (SMLR). Experimental results, conducted with two popular hyperspectral and LiDAR data sets collected over the University of Houston and the city of Trento, respectively, indicate that the proposed framework outperforms the other methods, i.e., different dimensionality-reduction-based methods, independent third-order tensor factorization based methods, and some recently proposed hyperspectral and LiDAR data fusion and classification methods.

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“…Analysis of HSIs has been widely used in a large variety of fields, including materials analysis, precision agriculture, environmental monitoring and surveillance [2][3][4]. Among the hyperspectral community, the HSIs classification is most vibrant filed of research which is to assign a unique class to each pixel in the image [5]. However, due to the excessively redundant spectral band information and limited training samples, it also poses a great challenge to the classification of HSIs [6].…”

Section: Introductionmentioning

confidence: 99%

CSA-MSO3DCNN: Multiscale Octave 3D CNN with Channel and Spatial Attention for Hyperspectral Image Classification

Xiao

Wang

et al. 2020

Remote Sensing

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3D convolutional neural networks (CNNs) have been demonstrated to be a powerful tool in hyperspectral images (HSIs) classification. However, using the conventional 3D CNNs to extract the spectral–spatial feature for HSIs results in too many parameters as HSIs have plenty of spatial redundancy. To address this issue, in this paper, we first design multiscale convolution to extract the contextual feature of different scales for HSIs and then propose to employ the octave 3D CNN which factorizes the mixed feature maps by their frequency to replace the normal 3D CNN in order to reduce the spatial redundancy and enlarge the receptive field. To further explore the discriminative features, a channel attention module and a spatial attention module are adopted to optimize the feature maps and improve the classification performance. The experiments on four hyperspectral image data sets demonstrate that the proposed method outperforms other state-of-the-art deep learning methods.

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Semisupervised Stacked Autoencoder With Cotraining for Hyperspectral Image Classification

Cited by 86 publications

References 39 publications

Application of Deep Learning in the Prediction of Benign and Malignant Thyroid Nodules on Ultrasound Images

Application of Deep Learning in the Prediction of Benign and Malignant Thyroid Nodules on Ultrasound Images

Coupled Higher-Order Tensor Factorization for Hyperspectral and LiDAR Data Fusion and Classification

CSA-MSO3DCNN: Multiscale Octave 3D CNN with Channel and Spatial Attention for Hyperspectral Image Classification

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