scholarly journals Multiple Spectral Resolution 3D Convolutional Neural Network for Hyperspectral Image Classification

2021 ◽  
Vol 13 (7) ◽  
pp. 1248
Author(s):  
Hao Xu ◽  
Wei Yao ◽  
Li Cheng ◽  
Bo Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Spectral Resolution ◽  
Hyperspectral Image ◽  
Feature Fusion ◽  
Rapid Development ◽  
Hyperspectral Data ◽  
Learning Technology ◽  
Classification Methods ◽  
Cubic Form

In recent years, benefiting from the rapid development of deep learning technology in the field of computer vision, the study of hyperspectral image (HSI) classification has also made great progress. However, compared with ordinary RGB images, HSIs are more like 3D cubes; therefore, it is necessary and beneficial to explore classification methods suitable for the very special data structure of HSIs. In this paper, we propose Multiple Spectral Resolution 3D Convolutional Neural Network (MSR-3DCNN) for HSI classification tasks. In MSR-3DCNN, we expand the idea of multi-scale feature fusion and dilated convolution from the spatial dimension to the spectral dimension, and combine 3D convolution and residual connection; therefore, it can better adapt to the 3D cubic form of hyperspectral data and make efficient use of spectral information in different bands. Experimental results on four benchmark datasets show the effectiveness of the proposed approach and its superiority as compared with some state-of-the-art (SOTA) HSI classification methods.

scholarly journals Hyperspectral Image Classification Based on Superpixel Pooling Convolutional Neural Network with Transfer Learning

2021 ◽  
Vol 13 (5) ◽  
pp. 930
Author(s):  
Fuding Xie ◽  
Quanshan Gao ◽  
Cui Jin ◽  
Fengxia Zhao
Keyword(s):  
Neural Network ◽  
Spatial Structure ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Learning Technology ◽  
Structure Information ◽  
Comparative Results

Deep learning-based hyperspectral image (HSI) classification has attracted more and more attention because of its excellent classification ability. Generally, the outstanding performance of these methods mainly depends on a large number of labeled samples. Therefore, it still remains an ongoing challenge how to integrate spatial structure information into these frameworks to classify the HSI with limited training samples. In this study, an effective spectral-spatial HSI classification scheme is proposed based on superpixel pooling convolutional neural network with transfer learning (SP-CNN). The suggested method includes three stages. The first part consists of convolution and pooling operation, which is a down-sampling process to extract the main spectral features of an HSI. The second part is composed of up-sampling and superpixel (homogeneous regions with adaptive shape and size) pooling to explore the spatial structure information of an HSI. Finally, the hyperspectral data with each superpixel as a basic input rather than a pixel are fed to fully connected neural network. In this method, the spectral and spatial information is effectively fused by using superpixel pooling technique. The use of popular transfer learning technology in the proposed classification framework significantly improves the training efficiency of SP-CNN. To evaluate the effectiveness of the SP-CNN, extensive experiments were conducted on three common real HSI datasets acquired from different sensors. With 30 labeled pixels per class, the overall classification accuracy provided by this method on three benchmarks all exceeded 93%, which was at least 4.55% higher than that of several state-of-the-art approaches. Experimental and comparative results prove that the proposed algorithm can effectively classify the HSI with limited training labels.

scholarly journals Multiscale Deep Spatial Feature Extraction Using Virtual RGB Image for Hyperspectral Imagery Classification

2020 ◽  
Vol 12 (2) ◽  
pp. 280 ◽  
Author(s):  
Liqin Liu ◽  
Zhenwei Shi ◽  
Bin Pan ◽  
Ning Zhang ◽  
Huanlin Luo ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Hyperspectral Image ◽  
Feature Fusion ◽  
Hyperspectral Data ◽  
Learning Technology ◽  
Learning Networks ◽  
Spatial Features ◽  
Training Samples ◽  
Rgb Image

In recent years, deep learning technology has been widely used in the field of hyperspectral image classification and achieved good performance. However, deep learning networks need a large amount of training samples, which conflicts with the limited labeled samples of hyperspectral images. Traditional deep networks usually construct each pixel as a subject, ignoring the integrity of the hyperspectral data and the methods based on feature extraction are likely to lose the edge information which plays a crucial role in the pixel-level classification. To overcome the limit of annotation samples, we propose a new three-channel image build method (virtual RGB image) by which the trained networks on natural images are used to extract the spatial features. Through the trained network, the hyperspectral data are disposed as a whole. Meanwhile, we propose a multiscale feature fusion method to combine both the detailed and semantic characteristics, thus promoting the accuracy of classification. Experiments show that the proposed method can achieve ideal results better than the state-of-art methods. In addition, the virtual RGB image can be extended to other hyperspectral processing methods that need to use three-channel images.

scholarly journals Face Detection and Recognition Method Based on Improved Convolutional Neural Network

2021 ◽  
Vol 15 ◽  
pp. 774-781
Author(s):  
Zhengqiu Lu ◽  
Chunliang Zhou ◽  
Xuyang Xuyang ◽  
Weipeng Zhang
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Rapid Development ◽  
Weighted Average ◽  
Recognition Algorithm ◽  
Equal Treatment ◽  
Learning Technology ◽  
Main Research ◽  
Common Face

with rapid development of deep learning technology, face recognition based on deep convolutional neural network becomes one of the main research methods. In order to solve the problems of information loss and equal treatment of each element in the input feature graph in the traditional pooling method of convolutional neural network, a face recognition algorithm based on convolutional neural network is proposed in this paper. First, MTCNN algorithm is used to detect the faces and do gray processing, and then a local weighted average pooling method based on local concern strategy is designed and a convolutional neural network based on VGG16 to recognize faces is constructed which is finally compared with common convolutional neural network. The experimental results show that this method has good face recognition accuracy in common face databases.

scholarly journals Composite Clustering Sampling Strategy for Multiscale Spectral-Spatial Classification of Hyperspectral Images

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Chenming Li ◽  
Xiaoyu Qu ◽  
Yao Yang ◽  
Dan Yao ◽  
Hongmin Gao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Sampling Strategy ◽  
Classification Methods ◽  
Hyperspectral Image Classification ◽  
Training Set ◽  
Training Time ◽  
Resource Requirements

In recent years, many high-performance spectral-spatial classification methods were proposed in the field of hyperspectral image classification. At present, a great quantity of studies has focused on developing methods to improve classification accuracy. However, some research has shown that the widely adopted pixel-based random sampling strategy is not suitable for spectral-spatial hyperspectral image classification algorithms. Therefore, a composite clustering sampling strategy is proposed, which can greatly reduce the overlap between the training set and the test set, while making sample points in the training set sufficiently representative in the spectral domain. At the same time, in order to solve problems of a three-dimensional Convolutional Neural Network which is commonly used in spectral-spatial hyperspectral image classification methods, such as long training time and large computing resource requirements, a multiscale spectral-spatial hyperspectral image classification model based on a two-dimensional Convolutional Neural Network is proposed, which effectively reduces the training time and computing resource requirements.

scholarly journals Experimental Discussion on Fire Image Recognition Based on Deep Learning

2021 ◽  
Vol 2066 (1) ◽  
pp. 012071
Author(s):  
Yongyi Cui ◽  
Fang Qu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Rapid Development ◽  
Fire Detection ◽  
Learning Technology ◽  
Fire Recognition

Abstract Fire detection technology based on video images is an emerging technology that has its own unique advantages in many aspects. With the rapid development of deep learning technology, Convolutional Neural Networks based on deep learning theory show unique advantages in many image recognition fields. This paper uses Convolutional Neural Networks to try to identify fire in video surveillance images. This paper introduces the main processing flow of Convolutional Neural Networks when completing image recognition tasks, and elaborates the basic principles and ideas of each stage of image recognition in detail. The Pytorch deep learning framework is used to build a Convolutional Neural Network for training, verification and testing for fire recognition. In view of the lack of a standard and authoritative fire recognition training set, we have conducted experiments on fires with various interference sources under various environmental conditions using a variety of fuels in the laboratory, and recorded videos. Finally, the Convolutional Neural Network was trained, verified and tested by using experimental videos, fire videos on the Internet as well as other interference source videos that may be misjudged as fires.

scholarly journals A NOVEL DEEP CONVOLUTIONAL NEURAL NETWORK FOR SPECTRAL–SPATIAL CLASSIFICATION OF HYPERSPECTRAL DATA

2018 ◽  
Vol XLII-3 ◽  
pp. 897-900 ◽  
Author(s):  
N. Li ◽  
C. Wang ◽  
H. Zhao ◽  
X. Gong ◽  
D. Wang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Three Dimensional ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Deep Convolutional Neural Network ◽  
Proposed Model

Spatial and spectral information are obtained simultaneously by hyperspectral remote sensing. Joint extraction of these information of hyperspectral image is one of most import methods for hyperspectral image classification. In this paper, a novel deep convolutional neural network (CNN) is proposed, which extracts spectral-spatial information of hyperspectral images correctly. The proposed model not only learns sufficient knowledge from the limited number of samples, but also has powerful generalization ability. The proposed framework based on three-dimensional convolution can extract spectral-spatial features of labeled samples effectively. Though CNN has shown its robustness to distortion, it cannot extract features of different scales through the traditional pooling layer that only have one size of pooling window. Hence, spatial pyramid pooling (SPP) is introduced into three-dimensional local convolutional filters for hyperspectral classification. Experimental results with a widely used hyperspectral remote sensing dataset show that the proposed model provides competitive performance.

scholarly journals Comparison of cephalometric measurements between conventional and automatic cephalometric analysis using convolutional neural network

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Sangmin Jeon ◽  
Kyungmin Clara Lee
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Rapid Development ◽  
Cephalometric Analysis ◽  
Anatomical Landmarks ◽  
Limits Of Agreement ◽  
Mandibular Incisor ◽  
Lateral Cephalograms ◽  
Dental Measurements ◽  
Cephalometric Measurements

Abstract Objective The rapid development of artificial intelligence technologies for medical imaging has recently enabled automatic identification of anatomical landmarks on radiographs. The purpose of this study was to compare the results of an automatic cephalometric analysis using convolutional neural network with those obtained by a conventional cephalometric approach. Material and methods Cephalometric measurements of lateral cephalograms from 35 patients were obtained using an automatic program and a conventional program. Fifteen skeletal cephalometric measurements, nine dental cephalometric measurements, and two soft tissue cephalometric measurements obtained by the two methods were compared using paired t test and Bland-Altman plots. Results A comparison between the measurements from the automatic and conventional cephalometric analyses in terms of the paired t test confirmed that the saddle angle, linear measurements of maxillary incisor to NA line, and mandibular incisor to NB line showed statistically significant differences. All measurements were within the limits of agreement based on the Bland-Altman plots. The widths of limits of agreement were wider in dental measurements than those in the skeletal measurements. Conclusions Automatic cephalometric analyses based on convolutional neural network may offer clinically acceptable diagnostic performance. Careful consideration and additional manual adjustment are needed for dental measurements regarding tooth structures for higher accuracy and better performance.

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