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Sub-graph matching-based building changes detection using high-resolution remote sensing images

International Journal of Earth Sciences and engineering 2016年第5期9卷 2172-2181页

作者： Huang, Feng-Hua Mao, Zheng-Yuan Shi, Wen-Zao Fuzhou University Fuzhou350116 China Yango College Fuzhou350015 China Key Lab of Spatial Data Mining & Information Sharing of Ministry of Education Fuzhou University Fuzhou350002 China National Engineering Research Centre of Geospatial Information Technology Fuzhou University Fuzhou350002 China Spatial Information Engineering Research Centre of Fujian Province Fuzhou University Fuzhou350002 China

High-resolution remote sensing (HRRS) images of urban regions have large viewing angle variations, significant noise jamming, and obvious building shadows. Hence, deviation and distortion usually occur to the buildings in HRRS images collected at different phases (multi-temporal HRRS images). The traditional detection methodology is ineffective for accurate and efficient building changes detection in multi-temporal HRRS images. In order to address these problems, this paper proposes a sub-graph matching-based building changes detection (SGMBCD) scheme. First, this paper presents a Graphcut-based buildings extracting (GCBE) method from multi-temporal HRRS images. Next, a sub-graph matching-based registration (SGMR) method is devised to register the previously extracted buildings from multi-temporal HRRS images and to obtain matched ASIFT feature point pairs and singular points. Finally, singular points and overlay analysis-based method (SPOA) is developed to detect building changes in multi-temporal HRRS images. The types of building changes included in this paper are changes (e.g., erection, dismantling, repairing, and reconstruction) and non-changes. In order to demonstrate the effectiveness of the proposed SGMBCD scheme, it is compared with five typical algorithms (i.e., BCDBPM, SDBBCD, BCDBICO, NCUTBCD, and RSMBCD) on three sets of multi-temporal WorldView2 test images. Experimental results show that in comparison with the other methods, SGMBCD can effectively address the challenging problem of building changes detection in multi-temporal HRRS images. The average recall ratio, precision ratio and F value is 91.47%, 86.49% and 88.91% respectively, and the average time consumption is 60.3 s. This demonstrates SGMBCD can detect building changes in multi-temporal HRRS images accurately and efficiently. © 2016 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.

关键词： Buildings

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Change detection of hyperspectral remote sensing images based on deep belief network

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International Journal of Earth Sciences and engineering 2016年第5期9卷 2096-2105页

作者： Huang, Fenghua Xu, Chao Zhu, Zhangzhi Fuzhou University Fuzhou350116 China Yango College Fuzhou350015 China Key Lab of Spatial Data Mining & Information Sharing of Ministry of Education Fuzhou University Fuzhou350002 China National Engineering Research Centre of Geospatial Information Technology Fuzhou University Fuzhou350002 China Spatial Information Engineering Research Centre of Fujian Province Fuzhou University Fuzhou350002 China

To address the performance bottlenecks of existing methods for change detection of hyperspectral remote sensing (HSRS) images, a new scheme for change detection of HSRS based on deep belief network (CDHSRS-DBN) is proposed. First, the HSRS images collected at two different phases (dual-temporal HSRS images) are pre-processed and registered, and then the spectral-difference images of the dual-temporal images are computed. Next, the endmember spectrums and the abundance-difference images are extracted using the pixel unmixing method based on independent component analysis (ICA). The low-layer feature vector of deep learning in CDHSRS-DBN adopts the integrated feature vector that consists of the pixel spectral-difference vector, endmember abundance-difference vector and the pixel spectral feature angle vector. Finally, a deep belief network (DBN) model that contains multi-layer restricted Boltzmann machines (RBM) and a support vector machine (SVM) is devised, and the weights of connections between visible and hidden layers are adjusted through pre-training. The accuracy of change detection is further improved by fine-tuning all the weights via the SVM classifier. In order to evaluate the performance of the proposed CDHSRS-DBN method, four pairs of EO-1 Hyperion test images at different phases, which collected in four different experimental zones, are used as the test data and CDHSRS-DBN is compared with six other typical HSRS change detection algorithms (CDHSRS-SCD, CDHSRS-MPD, CDHSRS-ICA, IR-MAD, CD-PCA and PCCD). The experiments focus on the detection of land-use changes. The average recall and precision reach 90.39% and 87.10%, respectively. The average value of F-Score and time consumption is 0.8871 and 242.5, respectively. Experimental results demonstrate the better performance of CDHSRS-DBN to detect changes of multi-temporal HSRS images accurately and efficiently. © 2016 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.

关键词： Change detection

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A novel reversible data hiding method for color images based on dynamic payload partition and cross-channel correlation

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Journal of Information Hiding and Multimedia Signal Processing 2016年第6期7卷 1194-1205页

作者： Qu, Dao-Cheng Pan, Jeng-Shyang Weng, Shao-Wei Xu, Shao Qiu Shenzhen Graduate School Harbin Institute of Technology ShenZhen China Shenzhen Graduate School Shenzhen Graduate School Harbin Institute of Technology Fujian Provincial Key Lab of Data Mining and Applicatioins Fujian University of Technology China School of Information Engineering Guangdong University of Technology GuangZhou China

The traditional color image reversible data hiding (RDH) techniques mainly focus on utilizing inter-channel correlations to improve embedding performance, but mostly have poor time efficiency. Nevertheless, the information redundancy in the color image is not fully exploited. In this paper, a new sorting strategy and a payload partition scheme are proposed. Thus, a novel RDH method for color images is proposed based on existed PEE algorithm. Extensive experimental results demonstrate that the proposed method achieves better performance comprehensively considering distortion control and time efficiency than prior state-of-the-art methods. © 2016.

关键词： Steganography

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Notice of Removal: An improved spread transform dither modulation for robust and secure watermarking

Notice of Removal: An improved spread transform dither modul...

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IEEE International Conference on Image Processing

作者： Jian Cao Haodong Li Weiqi Luo Jiwu Huang Shenzhen Graduate School Harbin Institute of Technology Shenzhen China School of Data and Computer Science and Guangdong Key Lab. of Information Security and Technology Sun Yat-Sen University Guangzhou China College of Information Engineering and Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China

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Notice of Removal: A secure watermarking technique without loss of robustness

Notice of Removal: A secure watermarking technique without l...

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IEEE International Conference on Image Processing

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Pathological brain detection based on online sequential extreme learning machine

Pathological brain detection based on online sequential extr...

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IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Siyuan Lu Hainan Wang Xueyan Wu Shuihua Wang School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu China State Key Lab of CAD & CG Zhejiang University Hangzhou Zhejiang China Key laboratory of symbolic computation and knowledge engineering of ministry of education Jilin University Changchun Jilin China Key Laboratory of Statistical information technology and data mining State Statistics Bureau Chengdu Sichuan China Department of Electrical Engineering CUNY New York NY USA

ISBN: (纸本)9781509034857

Magnetic resonance imaging (MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography (CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies (WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine (OS-ELM) was trained to differentiate pathological brains from the healthy controls. The experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%, which suggested that our method is effective.

关键词： Entropy Discrete wavelet transforms Feature extraction Pathology Training Magnetic resonance imaging Diseases

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Mesh Denoising using Extended ROF Model with L1 Fidelity

Mesh Denoising using Extended ROF Model with L1 Fidelity

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作者： Wu, Xiaoqun Zheng, Jianmin Cai, Yiyu Fu, Chi-Wing Beijing Key Lab of Big Data Technology for Food Safety School of Computer and Information Engineering Beijing Technology and Business University Beijing China College of Engineering Nanyang Technological University Singapore Singapore

This paper presents a variational algorithm for feature-preserved mesh denoising. At the heart of the algorithm is a novel variational model composed of three components: fidelity, regularization and fairness, which are specifically designed to have their intuitive roles. In particular, the fidelity is formulated as an L1 data term, which makes the regularization process be less dependent on the exact value of outliers and noise. The regularization is formulated as the total absolute edge-lengthed supplementary angle of the dihedral angle, making the model capable of reconstructing meshes with sharp features. In addition, an augmented Lagrange method is provided to efficiently solve the proposed variational model. Compared to the prior art, the new algorithm has crucial advantages in handling large scale noise, noise along random directions, and different kinds of noise, including random impulsive noise, even in the presence of sharp features. Both visual and quantitative evaluation demonstrates the superiority of the new algorithm. © 2015 The Author(s) Computer Graphics Forum.

关键词： Dihedral angle

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A hybrid-integrated external cavity laser with ultra-wide wavelength tuning range and high side-mode suppression

A hybrid-integrated external cavity laser with ultra-wide wa...

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Optical Fiber Communication Conference, OFC 2016

作者： Guo, Yuyao Li, Xinhang Xu, Weihan Liu, Chuxin Jin, Minhui Lu, Liangjun Xie, Jingya Stroganov, Anton Chen, Jianping Zhou, Linjie State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China Terahertz Technology Innovation Research Institute Terahertz Spectrum and Imaging Technology Cooperative Innovation Center Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology Shanghai200093 China LIGENTEC SA EPFL Innovation Park Bâtiment L Ecublens1024 Switzerland

We present a III-V/Si3N4 hybrid-integrated tunable laser. The laser shows a record of ~170-nm tuning range with a side mode suppression ratio above 64 dB and an intrinsic linewidth below 2.8 kHz. © 2022 The Autho...

ISBN: (纸本)9781943580071

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Classification Based on Multilayer Extreme Learning Machine for Motor Imagery Task from EEG Signals

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Procedia Computer Science 2016年 88卷 176-184页

作者： Lijuan Duan Menghu Bao Jun Miao Yanhui Xu Juncheng Chen College of Computer Science and Technology Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Trusted Computing National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing 100124 China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing 100190 China Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing 100124 China

Classification of motor imagery electroencephalogram (EEG) is one of the most important technologies for BCI. To improve the accuracy, this paper introduces a classification system based on Multilayer Extreme Learning Machine (ML-ELM). In the system, the combination of PCA and LDA is chosen as the method of feature extraction and the ML-ELM is used to classify. The ML-ELM has not only the advantage which ELM has but also better performance than ELM. In the experiment, our method is compared with the methods based on ELM, such as kernel-ELM, Constrained-ELM and V-ELM, and some state-of–the-art methods on the same dataset. The experimental results show that ML-ELM is much more suitable for motor imagery EEG data and has better performance than the others.

关键词： Electroencephalogram Classification Motor Imagery Extreme Learning Machine Multilayer Extreme Learning Machine

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Constraint Free Preference Preserving Hashing for Fast Recommendation

Constraint Free Preference Preserving Hashing for Fast Recom...

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IEEE Global Communications Conference

作者： Yan Zhang Guowu Yang Defu Lian Hong Wen Jinsong Wu Center for Cyber Security University of Electronic Science and Technology of China Big Data Research Center University of Electronic Science and Technology of China National Key Lab of Commun. University of Electronic Science and Technology of China Department of Electrical Engineering Universidad de Chile

ISBN: (纸本)9781509013296

Recommender systems have been widely used to deal with information overload, by suggesting relevant items that match users' personal interest. One of the most popular recommendation techniques is matrix factorization (MF). The inner products of learned latent factors between users and items can estimate users' preferences for items with high accuracy, but the preferences ranking is time consuming. Thus, hashing-based fast search technologies were exploited in recommender systems. However, most previous approaches consist of two stages: continuous latent factor learning and binary quantization, but they didn't well deal with the change of inner product arising from quantization. To this end, in this paper, we propose a constraint free preference preserving hashing method, which quantizes both norm and similarity in dot product. We also design an algorithm to optimize the bit length for norm quantization. The performance of our method is evaluated on three real world datasets. The results confirm that the proposed model can improve recommendation performance by 11%-15%, as compared with the state-of-the-art hashing approaches.

关键词： Recommender system Preference ranking Nor-m quantization Optimal bit Recommender systems Dot product Quantization hashing Preferences User preferences preference Consumers

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