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Detecting flood change with multitemporal SAR images based on difference images fusion and fuzzy degree of nearness

Detecting flood change with multitemporal SAR images based o...

MIPPR 2009 - Automatic Target Recognition and image Analysis: 6th International Symposium on Multispectral image Processing and Pattern Recognition

作者： Li, Jinji Jiao, Licheng Zhang, Xiangrrong Hou, Biao Yang, Dongdong Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9780819478061

In this paper, we present a novel approach for detecting the changed regions caused by flooding events in multi-temporal SAR images. And the proposed method concludes two parts: 1) constructing difference image (DI) by fusion strategy proposed in this paper;2) producing change-detection map based on FCM and fuzzy degree of nearness. Experimental comparisons on real multi-temporal SAR images indicate that the proposed method can reduce the affection by speckle noise. Meanwhile, the proposed method can accurately detect the interested changed regions. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Synthetic aperture radar

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An improved contour detection method based on level set and watershed transform

An improved contour detection method based on level set and ...

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MIPPR 2009 - Automatic Target Recognition and image Analysis: 6th International Symposium on Multispectral image Processing and Pattern Recognition

作者： Wang, Shuang Fu, Shenggao Jiao, Licheng Zhang, Xiaojing Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9780819478061

In this paper, an improved contour detection method based on level set and watershed transform is proposed. It is performed on coarse-to-fine approach: 1) primary contours detected by using level set evolution;2) accurate contours detected by watershed transform. The combined method utilizes the advantages of level set method and watershed transform. Experimental results show that our method could detect accurate contours and gain great improvement compared with level set evolution and watershed transform. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Numerical methods

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Spectrum Sensing Based on Deep Learning Classification for Cognitive Radios

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China Communications 2020年第2期17卷 138-148页

作者： Shilian Zheng Shichuan Chen Peihan Qi Huaji Zhou Xiaoniu Yang Science and Technology on Communication Information Security Control Laboratory Jiaxing 314033China State Key Laboratory of Integrated Service Networks Xidian UniversityXi’an 710071China Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education of ChinaXidian UniversityXi’an 710071China

Spectrum sensing is a key technology for cognitive *** present spectrum sensing as a classification problem and propose a sensing method based on deep learning *** normalize the received signal power to overcome the effects of noise power *** train the model with as many types of signals as possible as well as noise data to enable the trained network model to adapt to untrained new *** also use transfer learning strategies to improve the performance for real-world *** experiments are conducted to evaluate the performance of this *** simulation results show that the proposed method performs better than two traditional spectrum sensing methods,i.e.,maximum-minimum eigenvalue ratio-based method and frequency domain entropy-based *** addition,the experimental results of the new untrained signal types show that our method can adapt to the detection of these new ***,the real-world signal detection experiment results show that the detection performance can be further improved by transfer ***,experiments under colored noise show that our proposed method has superior detection performance under colored noise,while the traditional methods have a significant performance degradation,which further validate the superiority of our method.

关键词： spectrum sensing deep learning convolutional neural network cognitive radio spectrum management

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An improved watershed-based SAR image segmentation algorithm

An improved watershed-based SAR image segmentation algorithm

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MIPPR 2009 - Automatic Target Recognition and image Analysis: 6th International Symposium on Multispectral image Processing and Pattern Recognition

作者： Wang, Shuang Zhang, Xiaojing Jiao, Licheng Zhang, Xiangrong Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9780819478061

The watershed transform is a well-established tool for image segmentation. However, watershed segmentation is often not effective for Synthetic Aperture Radar (SAR) images which are generally corrupted by coherent speckle noise. This paper presents a novel approach for SAR image segmentation using watershed segmentation algorithm combined with Otsu. The aim of this study is to improve the generalization of watershed techniques and to construct a well segmentation of SAR images. Typically, Otsu is used to produce inner mark and external mark. Then we use the mark to modify the gradient image of the SAR image. Additionally, rather than flooding the gradient image, we use the gradient image modified by the mark as input to the watershed algorithm. Experimental results demonstrate the superior performance of the improved watershed-based SAR segmentation. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： image segmentation

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Improvement of Bandelets in cost function and coding strategy for SAR image compression

Improvement of Bandelets in cost function and coding strateg...

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2009 Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2009

作者： Yang, Shuyuan Wu, Ruixia Meng, Huixiao Jiao, Licheng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9781424427314

Synthetic aperture radar (SAR) data collections can cover large areas at high resolution, generating massive amounts of data. Many existed transform-based compression techniques can effectively reduce the costs of storage and transmission with slight loss of information. As a new developed multi-resolution geometry analysis tool, Bandelet can make full use of intrinsic geometry regularity of images and exhibit enormous potential in image compression. In this paper, we propose a low complexity Bandelet transform for SAR image compression. A new cost function is defined to determine the optimal geometric flows in each dyadic squared sub-block of images, and an optimization of low frequency component followed by EBCOT coding of the Bandelet coefficients are employed. Some experiments are taken on some Ku-band SAR images came from Sandia National Laboratories and the results show the superiority of our proposed method over JPEG2000 and the second generation Bandelet in PSNR, ENL and EPI. ©2009 IEEE.

关键词： Cost functions

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A method of COA based on multi-agent evolutionary algorithm

A method of COA based on multi-agent evolutionary algorithm

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MIPPR 2009 - Automatic Target Recognition and image Analysis: 6th International Symposium on Multispectral image Processing and Pattern Recognition

作者： Yu, Xin Wang, Hui Jiao, Licheng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9780819478061

Planning detailed military course of action (COA) is very complex and time consuming. In this paper, a method based on multi-agent evolutionary algorithm was presented to solve COA' resource management and scheduling problems. Each individual can be seen as an agent, in order to realize the local perceptivity of agents, the environment is organized as a latticelike structure. Each agent is fixed on a lattice point and it can only interact with its *** agent behaviors which are competition behavior and self-learning behavior are designed. In this work, constraint functions are considered as functions to be optimized like the objectives and then added in competition strategy to deal with the multi-objective aspect of resource-constrained project scheduling problems. This approach avoids the use of a penalty function to deal with constraints. At the same time, the added constraint functions could make the whole algorithm evolving feasible. The simulation results demonstrated that this approach could improve searching ability of this algorithm, and the precision of this method. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Evolutionary algorithms

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Unsupervised polarimetric SAR image classification using fisher linear discriminant

Unsupervised polarimetric SAR image classification using fis...

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2009 Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2009

作者： Chen, Bo Wang, Shuang Jiao, Licheng Zhang, Shuang Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9781424427314

How to use the POLSAR data to classify and interpret the conditions of the earth is a very important research field of POLSAR. In this paper, we propose an improved algorithm on the basis of studying and analyzing some common algorithms. This technique introduces the fisher criterion in the feature selection and the hierarchical method in the classification of POLSAR image, which can improve Lee's combination method of the unsupervised classification based on polarimetric target decomposition and the maximum likelihood classifier based on the complex Wishart distribution. The effectiveness of this algorithm is demonstrated using a polerimetric SAR image. ©2009 IEEE.

关键词： Synthetic aperture radar

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Algorithm of partition based network boosting for imbalanced data classification

Algorithm of partition based network boosting for imbalanced...

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2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010

作者： Shuiping, Gou Hui, Yang Licheng, Jiao Xiong, Zhuang Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9781424469178

Network Boosting (NB) is an ensemble learning method which combines weak learners together based on a network and can learn the target hypothesis asymptotically. NB has higher generalization ability compared to Bagging and AdaBoost. But, when datasets are class-imbalanced, the performance of NB will decrease quickly. In order to solve this problem, we present a Partition based Network Boosting method (PNB) to classify imbalanced data. For PNB method, every classifier node of the classifier network is provided with the same number of training data which are all of same weights. The classifier in the network is built by the balanced training set sampled from the training data according to the weights record of the training data it holds. And then, the weights of the instances of every node classifier are updated based on the classification results of self-node and its neighbor nodes. The classifier network is trained repeatedly in such a way. Weight factor of hypothesis in the training progress is introduced to improve the performance. The final classification is formed by all the hypotheses of the classifier network learned during the training progress so that the label of new instances can be decided by the weight voting. The experimental results on UCI data and imbalanced biomedical data show that the PNB algorithm has better AUC and recall performance compared with NB learning machine. © 2010 IEEE.

关键词： Adaptive boosting

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SAR target recognition based on improved sparse LSSVM

SAR target recognition based on improved sparse LSSVM

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MIPPR 2009 - Automatic Target Recognition and image Analysis: 6th International Symposium on Multispectral image Processing and Pattern Recognition

作者： Zhang, Xiangrong Zhang, Yifan Jiao, Licheng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9780819478061

An Improved Fast Sparse Least Squares Support Vector Machine (IFSLSSVM) is proposed for Synthetic Aperture Radar (SAR) target recognition. Least Squares Support Vector Machine (LSSVM) is a least square version of Support Vector Machine (SVM), but it lacks the sparseness compared with SVM. IFSLSSVM, which combines the incremental learning and decremental learning, selects those important samples as the support vectors, and implements pruning by a certain condition, can solve the non-sparse problem of LSSVM effectively. Benchmarking UCI datasets are firstly used for testing the performance of our algorithm, followed by SAR target recognition. Experimental results on MSTAR SAR dataset show that IFSLSSVM is an effective SAR target recognition approach (SAR-ATR), which not only reduces the number of support vectors but also enhances the recognition rate. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Support vector machines

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A Special Point and Transfer Component Analysis Based Dynamic Multi-objective Optimization Algorithm 11th

A Special Point and Transfer Component Analysis Based Dynami...

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11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Liu, Ruochen Li, Nanxi Peng, Luyao Wu, Kai Key Lab of Intelligent Perception and Image Understanding of Ministry of Education International Center of Intelligent Perception and Computation Xidian University Xi’an710071 China

ISBN: (纸本)9783030720612

To solve a dynamic multi-objective optimization problem better, algorithms need to quickly adapt to environmental changes and track its changing Pareto fronts fast. In this paper, an algorithm (SPTr-RMMEDA) based on special point and transfer component analysis is presented, in which, by using some special points and their neighborhoods solutions, the prediction strategy are used to generated the transfer learning prediction model, together with part new initial solutions to generate next population when change occurs. In order to better adapt to environmental changes, in addition to reusing some important historical information by transfer component analysis, the algorithm also introduces the adaptive diversity introduction strategy. The algorithm performs on 12 groups test problems. And the experimental results show that the proposed algorithm is superior to other four algorithms, such as Tr-NSGA-II, Tr-RMMEDA, DNSGA-II-A and DNSGA-II-B on most test problems in term of convergence and computation complexity. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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