Wiener filter based deep convolutional network approach for classification of satellite images

2020 ◽  
Author(s):  
M. Poomani ◽  
J. Sutha ◽  
K. Ruba Soundar
Keyword(s):  
Satellite Images ◽  
Wiener Filter ◽  
Network Approach ◽  
Convolutional Network ◽  
Deep Convolutional Network

Deep Convolutional Network Approach in Spike Train Analysis of Physiotherapy Movements

2019 ◽  
pp. 159-170
Author(s):  
Fadilla Atyka Nor Rashid ◽  
Nor Surayahani Suriani ◽  
Mohd Norzali Mohd ◽  
Mohd Razali Tomari ◽  
Wan Nurshazwani Wan Zakaria ◽  
...  
Keyword(s):  
Spike Train ◽  
Network Approach ◽  
Convolutional Network ◽  
Spike Train Analysis ◽  
Deep Convolutional Network

scholarly journals Hybrid Depth-Separable Residual Networks for Hyperspectral Image Classification

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Cuijie Zhao ◽  
Hongdong Zhao ◽  
Guozhen Wang ◽  
Hong Chen
Keyword(s):  
Hyperspectral Image ◽  
Computational Cost ◽  
Ground Truth ◽  
Hyperspectral Data ◽  
Deep Convolutional Neural Networks ◽  
Convolutional Network ◽  
Benchmark Datasets ◽  
Deep Convolutional Network ◽  
3D Cnn

At present, the classification of the hyperspectral image (HSI) based on the deep convolutional network has made great progress. Due to the high dimensionality of spectral features, limited samples of ground truth, and high nonlinearity of hyperspectral data, effective classification of HSI based on deep convolutional neural networks is still difficult. This paper proposes a novel deep convolutional network structure, namely, a hybrid depth-separable residual network, for HSI classification, called HDSRN. The HDSRN model organically combines 3D CNN, 2D CNN, multiresidual network ROR, and depth-separable convolutions to extract deeper abstract features. On the one hand, due to the addition of multiresidual structures and skip connections, this model can alleviate the problem of over fitting, help the backpropagation of gradients, and extract features more fully. On the other hand, the depth-separable convolutions are used to learn the spatial feature, which reduces the computational cost and alleviates the decline in accuracy. Extensive experiments on the popular HSI benchmark datasets show that the performance of the proposed network is better than that of the existing prevalent methods.

Distinguishing Cloud and Snow in Satellite Images via Deep Convolutional Network

2017 ◽  
Vol 14 (10) ◽  
pp. 1785-1789 ◽  
Author(s):  
Yongjie Zhan ◽  
Jian Wang ◽  
Jianping Shi ◽  
Guangliang Cheng ◽  
Lele Yao ◽  
...  
Keyword(s):  
Satellite Images ◽  
Convolutional Network ◽  
Deep Convolutional Network

scholarly journals Classification of Benign and Malignant Lung Nodules Based on Deep Convolutional Network Feature Extraction

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Enhui Lv ◽  
Wenfeng Liu ◽  
Pengbo Wen ◽  
Xingxing Kang
Keyword(s):  
Gradient Descent ◽  
Rapid Development ◽  
Descent Method ◽  
Stochastic Gradient Descent ◽  
Lung Nodules ◽  
Gradient Descent Method ◽  
Training Methods ◽  
Convolutional Network ◽  
Deep Convolutional Network

With the rapid development of detection technology, CT imaging technology has been widely used in the early clinical diagnosis of lung nodules. However, accurate assessment of the nature of the nodule remains a challenging task due to the subjective nature of the radiologist. With the increasing amount of publicly available lung image data, it has become possible to use convolutional neural networks for benign and malignant classification of lung nodules. However, as the network depth increases, network training methods based on gradient descent usually lead to gradient dispersion. Therefore, we propose a novel deep convolutional network approach to classify the benignity and malignancy of lung nodules. Firstly, we segmented, extracted, and performed zero-phase component analysis whitening on images of lung nodules. Then, a multilayer perceptron was introduced into the structure to construct a deep convolutional network. Finally, the minibatch stochastic gradient descent method with a momentum coefficient is used to fine-tune the deep convolutional network to avoid the gradient dispersion. The 750 lung nodules in the lung image database are used for experimental verification. Classification accuracy of the proposed method can reach 96.0%. The experimental results show that the proposed method can provide an objective and efficient aid to solve the problem of classifying benign and malignant lung nodules in medical images.

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