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IET International Radar Conference 2020, IET IRC 2020

作者： Li, Yuan Guo, Zhishun Wu, Lin Guo, Zhengwei College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng475000 China College of Environment and Planning Henan University Kaifeng475004 China

ISBN: (纸本)9781839535635

Synthetic Aperture Radar (SAR) image classification is a key technique and research focus in the application of remote sensing. This paper proposes a new classification method of SAR image based on Object-based Gaussian-Markov Random Field model with Region Coefficients (OGMRF-RC), the new method use Fuzzy Probability (FP) represents the region's category probability, FP is obtained by regional posterior probability and edge information of neighborhood. Use FP to replace the unique class label of the region, which avoids the parameters estimation inaccurate of the feature field caused by the unique initial class label. And the parameter calculation method of feature field takes into account the feature information of all regions in the image. In this paper, we take the area around Baliwan in Kaifeng as study area, Sentinel-1 is used as the remote sensing data sources. FCM, K-means, classic MRF and OGMRF-RC are used as contrast methods, the experimental result demonstrates the accuracy of our method is better than these methods. © 2020 Institution of Engineering and Technology. All rights reserved.

关键词： Synthetic aperture radar

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Convolutional Long-Short Term Memory Network for convective weather prediction based on 3D Doppler radar data 2020

Convolutional Long-Short Term Memory Network for convective ...

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Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies

作者： Xiajiong Shen Kunying Meng Zhongli Guo Lei Zhang Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng PR China School of Computer and Information Engineering Henan University Kaifeng PR China Southwest Regional Air Traffic Management Bureau CAAC Senior engineer of aviation meteorology

ISBN: (纸本)9781450387828

Convectional weather is one of the weather which often occurs during the warm season, the scope of this kind of weather is normally small, short duration, etc, so it is difficult to predicted, the effects of convectional weather are very large, from the national economy to military activities will be threatened by it, so how to achieve more accurate of convection weather forecast will be of great significance. Doppler radar is one of the main tools for monitoring and early warning severe convective weather. It can provide important strong convective information for prediction. through its observed real-time echo intensity (reflectivity factor Z), radial velocity (V) and velocity spectrum width (W). Echo intensity is an important basis for judging whether severe, convective weather occurs, and radial velocity can identify wind damage and is also an important basis for judging the occurrence of severe convective weather. From the radial velocity, we can see the convergence, divergence and rotation characteristics of airflow, which are closely related to the occurrence and development of severe convective weather. Therefore, Doppler radar data plays a very important role in the detection and prediction of severe weather. At present, there are still problems about how to effectively extract these information from Doppler radar *** paper proposes a method of near prediction based on deep learning. The neural network is used to automatically learn data features for prediction. Convolution neural network is combined with long-short-time memory network. Firstly, a three-dimensional convolution is proposed to extract the spatial features of three-dimensional original data, and then the long-short-time memory network is used to extract the features of data in time dimension, thus improving the prediction accuracy. Deep learning method avoids the process of manually extracting data features, and uses historical 3D Doppler radar image data to predict convective weather fo

关键词： Convective weather

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Surface soil moisture inversion based on DEFS-GA-BP model using Sentinel-1/2 remote sensing data 5

Surface soil moisture inversion based on DEFS-GA-BP model us...

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IET International Radar Conference 2020, IET IRC 2020

作者： Zhang, Chenyang Min, Lin Zhao, Jianhui College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng475004 China Network Information Center Office Henan University Kaifeng475004 China

ISBN: (纸本)9781839535635

The major work is to retrieve the surface soil moisture under crop cover in this paper. This paper proposes an improved GA-BP model called as DEFS-GA-BP model. This model is proposed to retrieve the surface soil moisture distribution using Sentinel-1 and Sentinel-2 remote sensing data. After extracting nine polarization features and one roughness parameter from Sentinel-1 data, five vegetation indexes from Sentinel-2 data. The differential evolution feature selection (DEFS) algorithm is applied to select the optimal feature subset. By using the genetic algorithm (GA) to optimize the node weights, the back propagation (BP) neural network is trained using the optimal feature subset and then used to generate the surface soil moisture distribution. The results of inversion algorithm indicate that the proposed DEFS-GA-BP model has a better inversion precision than GA-BP model, and demonstrates its application potential in surface soil moisture inversion. © 2020 Institution of Engineering and Technology. All rights reserved.

关键词： Genetic algorithms

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Reliability on Deep Learning Models: A Comprehensive Observation

Reliability on Deep Learning Models: A Comprehensive Observa...

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International Symposium on System and Software Reliability (ISSSR)

作者： Yuhong Zhang Chunjing Xiao School of Artificial Intelligence and Big Data Key Laboratory of Grain Information Processing and Control Ministry of Education Henan University of Technology Zhengzhou China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

ISBN: (数字)9780738104973

ISBN: (纸本)9781665423236

This paper provides a comprehensive observation to examine the reliability of deep learning (DL) models. First, we will briefly introduce the essential background and kernel techniques in deep learning, such as downsampling and nonlinear discontinuity. Each of them may have some relation to the reliability of DL models. Then we discuss the inherent structural flaws of deep learning and the risk of unreliability that can result from it. Subsequently, we discuss various ways of generating adversarial samples that affect the DL model's reliability and corresponding preventive measures. Finally, we complete this observation by identifying current challenges and future trends for research.

关键词： Reliability Deep learning Biological neural networks Perturbation methods Neural networks Feature extraction data models

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Unmanned Aerial Vehicle Classification and Detection Based on Deep Transfer Learning

Unmanned Aerial Vehicle Classification and Detection Based o...

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Intelligent Computing and Human-Computer Interaction (ICHCI), International Conference on

作者： Wei Meng Meng Tia School of computer and information engineering Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan Engineering Laboratory of Spatial Information Processing Kaifeng China

Currently, major security incidents caused by the “unlicensed flying” of Unmanned Aerial Vehicle (UAV) have emerged one after another, which poses a grave threat to the security issues of public facilities and sensitive areas. Whether it can timely detect and prevent “unlicensed flying” of UAV has become a social concern. In response to this demand, the transfer learning method is adopted in this paper to conduct twoclassification and detection on UAV images. Image recognition technology based on transfer learning is an effective method to improve recognition accuracy by applying deep learning models to small samples. Different from the large number of training samples required by deep learning, transfer learning transfers the weights of the pre-trained deep neural network, and uses only small sample data to obtain good results in UAV image recognition. First of all, this paper proposes to construct a UAV data set according to different types of UAV shape structures, to perfect the classification and detection effect and the generalization ability of the model. Then, based on the transfer learning method, experimental comparison is made between three classic deep convolutional neural network classification models (Inception V3, ResNet 101 and VGG16) and two classic deep convolutional neural network detection models (Faster RCNN and SSD). Finally, an experimental evaluation is conducted on the collected UAV test data set. Compared with the traditional recognition model, the image classification model based on transfer learning employed in this paper has achieved important improvements in accuracy, recall and precision. Especially in the InceptionV3 model of transfer training, the recall reaches 96.98%. In addition, the image detection model based on transfer learning has achieved good detection results in accuracy, recall and F1-score.

关键词： Training Deep learning Image recognition Shape Transfer learning Buildings Unmanned aerial vehicles

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Spatiotemporal Evolution analysis of NO 2 Column Density before and after COVID-19 Pandemic in henan.Province Based on SI-APSTE Model

Research Square

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Research Square 2021年

作者： Liu, Yang Zhao, Jinhuan Song, Kunlin Cheng, Cheng Li, Shenshen Cai, Kun Henan Engineering Laboratory of Spatial Information Processing Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Kaifeng475004 China State Key Laboratory of Remote Sensing Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100101 China School of Information and Electronics Beijing Institute of Technology Beijing100081 China

Air pollution is the result of comprehensive evolution of a dynamic and complex system composed of emission sources, topography, meteorology and other environmental factors. The establishment of spatiotemporal evolution model is of great significance for the study of air pollution mechanism, trend prediction, identification of pollution sources and pollution control. In this paper, the air pollution system is described based on cellular automata and restricted agents, and a Swarm Intelligence based Air Pollution SpatioTemporal Evolution (SI-APSTE) model is constructed. Then the spatiotemporal evolution analysis method of air pollution is studied. Taking henan.Province before and after COVID-19 pandemic as an example, the NO2 products of TROPOMI and OMI were analysed based on SI-APSTE model. The tropospheric NO2 Vertical Column Densities (VCDs) distribution characteristics of spatiotemporal variation of henan.province before COVID-19 pandemic were studied. Then the tropospheric NO2 VCDs of TROPOMI was used to study the pandemic period, month-on-month and year-on-year in 18 urban areas of henan.Province. The results show that SI-APSTE model can effectively analyse the spatiotemporal evolution of air pollution by using environmental big data and swarm intelligence, and also can establish a theoretical basis for pollution source identification and trend prediction. © 2021, CC BY.

关键词： Air pollution

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Fast Parallel Extraction Method of Normalized Vegetation Index

Fast Parallel Extraction Method of Normalized Vegetation Ind...

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International Conference on Computer Science & Education (ICCSE)

作者： Xianyu Zuo Tongyuan Qi Baojun Qiao Zhitao Deng Qiang Ge Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (数字)9781728172675

ISBN: (纸本)9781728172682

At present, accelerated processing of remote sensing big data has become an important research topic in the field of remote sensing. Remote sensing image processing based on large-scale clusters currently is the mainstream. However, how to fully tap the computing power of a single computing node in the cluster has become issues that cannot be ignored in the field of remote sensing image processing. Traditional GPU programming is difficult to develop, the development cycle is long, and the requirements for developers are very high. In order to improve the efficiency of GPU programming and shorten the development cycle of parallel programs, Nvidia, Grary, PGI and CAPS jointly launched a new programming standard-OpenAcc. In this paper, OpenAcc-NDVI, as a fast parallel extraction method is used to optimize NDVI algorithm. Based on different computing scenarios, two granularity acceleration models are proposed. It has been verified by multiple experiments that when the data size reaches 10000 * 10000, OpenAcc-NDVI can achieve an acceleration of about 5.3 times. After error analysis, the error of algorithm experiment result is 0, there is no loss of precision. The NDVI algorithm based on OpenAcc has excellent acceleration performance, efficient development process, and high calculation accuracy.

关键词： Acceleration Graphics processing units Histograms Parallel processing Vegetation mapping Remote sensing Arrays

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Spatial fuzzy C-means clustering and deep belief network for change detection in synthetic aperture radar images 5

Spatial fuzzy C-means clustering and deep belief network for...

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IET International Radar Conference 2020, IET IRC 2020

作者： Qi, Wenwen Wu, Lin Guo, Zhengwei Huang, Dan College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng475004 China College of Environment and Planning Henan University Kaifeng475004 China Department of Laboratory and Equipment Management Henan University Kaifeng475004 China

ISBN: (纸本)9781839535635

In this study, spatial fuzzy c-means (SFCM) clustering and deep belief network (DBN) method is presented for change detection in SAR images. There are three primary steps of this approach, they are given as follows: 1) Difference Image (DI) is firstly obtained by calculating the similarity of gray levels between multitemporal SAR images. 2) SFCM clustering is used to analyze DI, which combines the spatial information factor and the fuzzy cluster membership function, thus can effectively eliminate the interference of speckle noise. 3) DBN, a powerful data processing methodology that integrates deep learning techniques to extract underlying features and structures of complicated data, is trained to classify pixels to be changed or unchanged. The effectiveness and robustness of the proposed method are verified by experimental results with the Ottawa dataset. © 2020 Institution of Engineering and Technology. All rights reserved.

关键词： Membership functions

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Influence of Elevation and Orbit Interpolation on the Accuracy of R-D Location Model 6

Influence of Elevation and Orbit Interpolation on the Accura...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Fan, Minghu Zuo, Xianyu Tian, Junfeng Key Laboratory of Big Data Analysis and Processing of Henan Province College of Computer Science and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9781728129129

R-D model is the most commonly location model in geometric processing of SAR images. How to improve its location accuracy has been one of the focuses in application. Based on the in-depth analysis of the solving process of R-D equation, two kinds of influencing factors are selected to study. They are elevation and satellite orbit interpolation method. Then, aiming at the pixel location of high-resolution spaceborne SAR images, three kinds of DEM and two orbit interpolation algorithms were studied. The experimental results show that these two kinds of factors mainly affect the location accuracy of range direction, both the horizontal and vertical resolution of elevation have great influence on location accuracy, the quartic polynomial orbit interpolation can meet requirements of high accuracy location, and further improvement of the location accuracy need other auxiliary means. © 2019 IEEE.

关键词： Synthetic aperture radar

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Parallel Geometric Correction for Single Spaceborne SAR Image 6

Parallel Geometric Correction for Single Spaceborne SAR Imag...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

ISBN: (纸本)9781728129129

The fast processing of SAR images is of great significance for SAR applications, and parallel computing is the most common solution. The paper studied the parallel computing algorithm for the geometric correction of single spaceborne SAR image, which is based on a classical serial geometric correction algorithm. After fully analyzing the parallel features of the algorithm, a CPU-oriented and a GPU-oriented global parallel correction algorithm were designed. Then, two spaceborne SAR images were used to test the speedup performance of the two algorithms. The experimental results show that the two parallel algorithms can greatly improve the speed of the geometric correction of spaceborne SAR image, and the performance of CPU-oriented parallel algorithm is mainly related to the number of CPU/cores, while the performance of GPU-oriented parallel algorithm is mainly related to the data granularity, and the latter has very high speedup ratio and is an efficient method for geometric correction of spaceborne SAR image. © 2019 IEEE.

关键词： Synthetic aperture radar

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