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6th International Conference on Radar, RADAR 2011

作者： Wang, Shuang Zhou, Jiao Li, Jun Feng, Hongxiao Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education of China Xidian University Xi'an 710071 China

We propose a despeckling method based on dual-tree complex wavelet transform (DTCWT) for SAR image. The Gaussian model is used to model the non-logarithmic additive noise of the local isotropic region. Shrinkage funct... 详细信息

ISBN: (纸本)9781424484416

关键词： Additive noise

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Remote sensing image registration with spatial restraint based on moment invariants and fast generalized fuzzy clustering

Remote sensing image registration with spatial restraint bas...

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Conference on Technologies and Applications of Artificial Intelligence, TAAI 2015

作者： Yue, Wu Maoguo, Gong Jia, Ji Ma, Wenping Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Xidian University Xi'an710071 China

ISBN: (纸本)9781467396066

image registration is a key component in remote sensing image processing. In this paper, we present a remote sensing image registration method by incorporating spatial restraint based on moment invariants and fast generalized fuzzy clustering. Seven moment invariants are extracted as features of objects obtained by the fast generalized fuzzy c-means (FGFCM) algorithm. The objects are matched through these features. Then, we detect the keypoints in corresponding matching regions. Through the spatial restraint, the outliers are removed and the correct matches are increased. The proposed algorithm is evaluated on multi-spectral images, multi-temporal images, and multi-sensor images. Extensive experimental studies prove that the proposed algorithm is promising. © 2015 IEEE.

关键词： Remote sensing

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Quantum-inspired immune memory algorithm for self-structuring antenna optimization

Quantum-inspired immune memory algorithm for self-structurin...

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International Conference on Computer Science and Software Engineering, CSSE 2008

作者： Qiuyi, Wu Xiaoying, Pan Licheng, Jiao Yifei, Sun Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education of China Xidian University Xi'an 710071 China

ISBN: (纸本)9780769533360

Based on the concept and principle of quantum computing and immune system, a novel optimization technique, called a quantum-inspired immune memory algorithm is proposed to deal with the problem of the optimization of the switches of the self-structuring antenna. In the algorithm, the proposed memory strategy realizes the information transfer between the courses of evolution. Theoretical analysis proves that quantum-inspired immune memory algorithm converges to the global optimum. The feasibility, efficiency and effectiveness of the proposed algorithm for optimization of self-structuring antenna, whose performance is analyzed by the fast moment method (Mom) are examined. The results show that quantum-inspired immune memory algorithm performs much better than the genetic algorithms in terms of the quality of solution and convergence speed. In addition, parameter analysis demonstrates our algorithm has stable performance and is insensitive to the change of parameters. © 2008 IEEE.

关键词： Antennas

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A new constrained spectral clustering for SAR image segmentation

A new constrained spectral clustering for SAR image segmenta...

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

作者： Zou, Haishuang Zhou, Weida Zhang, Li Wu, Caili Liu, Ruochen Jiao, Licheng Institute of Intelligent Information Processing Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University 710071 China

ISBN: (纸本)9781424427314

Pairwise constraints have been successful to be applied in traditional clustering methods. However, little progress has been made in incorporating them into spectral clustering. In this paper, we propose a new method to combine pairwise constraints with spectral clustering and apply it to SAR image segmentation. Firstly, we learn a distance metric using pairwise constraints. In doing so, an affinity matrix is obtained by the Gaussian function on the learned distance metric. Then we perform the spectral decomposition on the affinity matrix and we get the spectral features of data points. Finally, the constrained k-means is used to cluster spectral features instead of k-means used commonly. We apply the proposed method to synthetic aperture radar (SAR) image segmentation. Experimental results show that it is effective for SAR image segmentation. ©2009 IEEE.

关键词： image segmentation

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Two-phase clustering algorithm with density exploring distance measure

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CAAI Transactions on Intelligence Technology 2018年第1期3卷 59-64页

作者： Jingjing Ma Xiangming Jiang Maoguo Gong Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China Xidian University PO Box 224 Xi'an 710071People's Republic of China

Here, the authors propose a novel two-phase clustering algorithm with a density exploring distance （DED） measure. In the first phase, the fast global K-means clustering algorithm is used to obtain the cluster number and the prototypes. Then, the prototypes of all these clusters and representatives of points belonging to these clusters are regarded as the input data set of the second phase. Afterwards, all the prototypes are clustered according to a DED measure which makes data points locating in the same structure to possess high similarity with each other. In experimental studies, the authors test the proposed algorithm on seven artificial as well as seven UCI data sets. The results demonstrate that the proposed algorithm is flexible to different data distributions and has a stronger ability in clustering data sets with complex non-convex distribution when compared with the comparison algorithms.

关键词：密度探索距离聚类算法计算方法人工智能

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SAR image despeckling based on edge detection and nonsubsampled second generation bandelets

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Journal of Systems Engineering and Electronics 2009年第3期20卷 519-526页

作者： Zhang Wenge~(1,2),Liu Fang~(1,2),Jiao Licheng~(2,3)& Gao Xinbo~(2,3) 1.School of Computer Science and Technology,Xidian Univ.,Xi’an 710071,P.R.China 2.key Lab.of intelligent perception and image understanding of ministry of education of China,Xi’an 710071,P.R.China 3.Inst,of intelligent Information Processing,Xidian Univ.,Xi’an 710071,P.R.China School of Computer Science and Technology Xidian Univ. Xi'an 710071 P. R. China Key Lab. of Intelligent Perception and Image Understanding of Ministry of Education of China Xi'an 710071 P. R. China Key Lab. of Intelligent Perception and Image Understanding of Ministry of Education of China Xi'an 710071 P. R. China Inst. of Intelligent Information Processing Xidian Univ. Xi'an 710071 P. R. China

To preserve the sharp features and details of the synthetic aperture radar （SAR） image effectively when despeckling, a despeckling algorithm with edge detection in nonsubsampled second generation bandelet transform （NSBT） domain is proposed. First, the Canny operator is utilized to detect and remove edges from the SAR image. Then the NSBT which has an optimal approximation to the edges of images and a hard thresholding rule are used to approximate the details while despeckling the edge-removed image. Finally, the removed edges are added to the reconstructed image. As the edges axe detected and protected, and the NSBT is used, the proposed algorithm reaches the state-of-the-art effect which realizes both despeckling and preserving edges and details simultaneously. Experimental results show that both the subjective visual effect and the mainly objective performance indexes of the proposed algorithm outperform that of both Bayesian wavelet shrinkage with edge detection and Bayesian least square-Gaussian scale mixture （BLS-GSM）.

关键词： computer image processing synthetic aperture radar speckle edge detection nonsubsampled second generation bandelet transform Canny operator threshold shrinkage.

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Immune clonal spectral clustering for PolSAR land cover classification

Immune clonal spectral clustering for PolSAR land cover clas...

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International Conference on Machine Tool Technology and Mechatronics Engineering, ICMTTME 2014

作者： Liu, Lu Sun, Dong Wang, Wen Qing Niu, Rui Can Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education International Research Center for Intelligent Perception and Computation Xidian University Xi'anShaanxi China

ISBN: (纸本)9783038352464

In this paper, a novel PolSAR land cover classification method is proposed based on spectral clustering (SC) and immune clonal selection principle. SC is employed to reduce dimension in the polarimetric feature space. However, it is susceptible to local optimum and sensitive to the initial partition. To address this issue, immune clonal clustering (ICC) is used due to its capability of global searching. The proposed method combines the complementary advantage of spectral clustering and immune clonal clustering. Experimental results demonstrate that the proposed method is more stable and efficient compared with the other methods. © (2014) Trans Tech Publications, Switzerland.

关键词： Clustering algorithms

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Immune secondary response and clonal selection inspired optimizers

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Progress in Natural Science:Materials International 2009年第2期19卷 237-253页

作者： Maoguo Gong a,Licheng Jiao a,Lining Zhang a,Haifeng Du b a 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 b School of Public Policy and Administration,Xi’an Jiaotong University,Xi’an 710049,China [a]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 [b]School of Public Policy and Administration Xi’an Jiaotong University Xi’an 710049 China

The immune system’s ability to adapt its B cells to new types of antigen is powered by processes known as clonal selection and affinity maturation. When the body is exposed to the same antigen,immune system usually calls for a more rapid and larger response to the antigen,where B cells have the function of negative adjustment. Based on the clonal selection theory and the dynamic process of immune response,two novel artificial immune system algorithms,secondary response clonal programming algorithm (SRCPA) and secondary response clonal multi-objective algorithm (SRCMOA),are presented for solving single and multi-objective optimization problems,respectively. Clonal selection operator (CSO) and secondary response operator (SRO) are the main operators of SRCPA and SRCMOA. Inspired by the clonal selection theory,CSO reproduces individuals and selects their improved maturated progenies after the affinity mat-uration process. SRO copies certain antibodies to a secondary pool,whose members do not participate in CSO,but these antibodies could be activated by some external stimulations. The update of the secondary pool pays more attention to maintain the population diversity. On the one hand,decimal-string representation makes SRCPA more suitable for solving high-dimensional function optimiza-tion problems. Special mutation and recombination methods are adopted in SRCPA to simulate the somatic mutation and receptor edit-ing process. Compared with some existing evolutionary algorithms,such as OGA/Q,IEA,IMCPA,BGA and AEA,SRCPA is shown to be able to solve complex optimization problems,such as high-dimensional function optimizations,with better performance. On the other hand,SRCMOA combines the Pareto-strength based fitness assignment strategy,CSO and SRO to solve multi-objective optimization problems. The performance comparison between SRCMOA,NSGA-Ⅱ,SPEA,and PAES based on eight well-known test problems shows that SRCMOA has better performance in converging to approximate Pareto

关键词： Clonal selection Secondary response Artificial immune systems Evolutionary algorithms

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DNN-based joint classification for multi-source image change detection 11th

DNN-based joint classification for multi-source image change...

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11th International Conference on Bio-inspired Computing – Theories and Applications, BIC-TA 2016

作者： Ma, Wenping Li, Zhizhou Zhang, Puzhao Hu, Tianyu Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi’anShaanxi Province710071 China

ISBN: (纸本)9789811036101

In this paper, we propose a novel joint classification framework for multi-source image change detection, the multi-source imagepair is generated by different sensors, such as optical sensor and synthetic aperture radar, respectively. This framework is established for feature learning, which is based on deep neural networks. Firstly, in order to segment the optical image, deep neural networks are essential to extract deep features for clustering segmentation. Then the stacked denoising autoencoders is trained to learn capability of classification via choosing part of reliable segmentation results of optical image as labels. Next, the other image of the image-pair is entered in the trained stacked denoising autoencoders to classification automatically. Afterwards, two images passed joint classification are obtained. Finally, the difference image is produced by comparing the two images passed joint classification. Experimental results illustrate that the method can be applied to multi-source image and outperforms the state-of-the-art methods. © Springer Nature Singapore Pte Ltd. 2016.

关键词： Deep neural networks

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Block Pruning and Collaborative Distillation for image Classification of Remote Sensing

Block Pruning and Collaborative Distillation for Image Class...

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2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024

作者： Wang, Xiaokai Wang, Rongfang Jiao, Changzhe Niu, Yi Mu, Caihong Xidian Univerisity The Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xi'an China

ISBN: (纸本)9798350360325

Convolutional Neural Networks (CNNs) have achieved remarkable performance in remote sensing image classification tasks. To address the issue of high model complexity, we propose a block-level pruning strategy based on the semantic similarity analysis that no fine-tuning is required during the pruning process. By employing this strategy, we effectively reduce the complexity of the model. Furthermore, to restore the overall performance of the pruned model, we propose a teacher-student collaborative distillation strategy that enables knowledge transfer through the collaboration of the original model and the dropped-blocks model to promote the performance of the compact pruned model. Experimental results demonstrate that our pruning and distillation strategies outperform other approaches, thereby achieving favorable performance while reducing model complexity. © 2024 IEEE.

关键词： blocks pruning image classification knowledge distillation light CNN remote sensing

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