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检索条件"主题词=Change detection algorithms"

共 1588 条记录，以下是281-290 订阅

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Temporal Network change detection Using Network Centralities

Temporal Network Change Detection Using Network Centralities

引用

International Conference on Data Science and Advanced Analytics (DSAA)

作者： Yoshitaro Yonamoto Kai Morino Kenji Yamanishi Graduate School of Information Science & Technology The University of Tokyo Tokyo Japan

ISBN: (纸本)9781509052073

In this paper, we propose a novel change detection method for temporal networks. In usual change detection algorithms, change scores are generated from an observed time series. When this change score reaches a threshold, an alert is raised to declare the change. Our method aggregates these change scores and alerts based on network centralities. Many types of changes in a network can be discovered from changes to the network structure. Thus, nodes and links should be monitored in order to recognize changes. However, it is difficult to focus on the appropriate nodes and links when there is little information regarding the dataset. Network centrality such as PageRank measures the importance of nodes in a network based on certain criteria. Therefore, it is natural to apply network centralities in order to improve the accuracy of change detection methods. Our analysis reveals how and when network centrality works well in terms of change detection. Based on this understanding, we propose an aggregating algorithm that emphasizes the appropriate network centralities. Our evaluation of the proposed aggregation algorithm showed highly accurate predictions for an artificial dataset and two real datasets. Our method contributes to extending the field of change detection in temporal networks by utilizing network centralities.

关键词： Time series analysis Games Knowledge engineering Eigenvalues and eigenfunctions Aggregates change detection algorithms Prediction algorithms

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Parallel implementation of the FLICM algorithm for SAR image change detection on intel MIC

Parallel implementation of the FLICM algorithm for SAR image...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Huming Zhu Le Lu Yucong Fan Pei Li Qingyu Zhang Licheng Jiao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an Shaanxi Province China

ISBN: (纸本)9781509033331

Image change detection has a wide range of applications in various fields, such as damage assessment, environmental monitoring and agricultural surveys. As the number of remote sensing images and the complexity of algorithm rise, the demand for processing power is increasing. In this paper, we present a parallel FLICM algorithm for SAR image change detection on Intel MIC (Many Integrated Core) which is the new type of many-core coprocessor. The proposed algorithm is implemented based on MIC-Offload mode using OpenMP (Open Multi-Processing). The parallel characteristics and implementation details of the proposed parallel FLICM algorithm are presented. Experiment results demonstrate that the optimized parallel algorithm can greatly reduce the computational time of the change detection algorithm. The speedup is up to 20× compared with the runtime of serial algorithm on CPU.

关键词： Microwave integrated circuits Synthetic aperture radar change detection algorithms Clustering algorithms Runtime Instruction sets Remote sensing

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A novel high resolution remote sensing image change detection algorithm based on image fusion and fuzzy clustering models

A novel high resolution remote sensing image change detectio...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Mengyuan Liu Yujiang Liu Shandong University SDU Jinan China Beijing University of Posts and Telecommunication BUPT Beijing China

ISBN: (纸本)9781509012862

In this paper, we propose a novel high resolution remote sensing image change detection algorithm based on image fusion and fuzzy clustering models. The original images are fused based on the Wavelet to get the comprehensive differencing map which reserves enough variation characteristics as well as reducing the noise. The idea of modifying traditional clustering algorithms will enhance the final result of the change map. This method is able to achieve the higher detection accuracy based on the revised fuzzy clustering algorithm and could obtain better differencing map with integration of image fusion. Experiments on real images point out the effectiveness and feasibility of the proposed methodology compared with other state-of-the-art ones visually and numerically.

关键词： Remote sensing Image fusion Spatial resolution change detection algorithms Algorithm design and analysis Clustering algorithms

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Automatic change detection based on conditional random field in high resolution remote sensing images

Automatic change detection based on conditional random field...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Guo Cao Xuesong Li Yanfeng Shang The School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China The third research institute of ministry of public security Shanghai China

An automatic change detection method based on conditional random field (CRF) is presented for high resolution remote sensing images in this paper. Marginalized denoising autoencoder is used to generate the difference image. The clustering results of Fuzzy C-means are applied to initialize the unary potentials of CRF. A scaled squared Euclidean distance between neighboring pixels in the observed images is introduced to define the pairwise potentials of CRF, which avoid training parameters and help improve the accuracy and the degree of automation. The experimental results obtained on different remote sensing images demonstrated the accuracy and efficiency of our proposed method.

关键词： Remote sensing change detection algorithms Training Classification algorithms Labeling Image resolution Kernel

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Unsupervised change detection using Spatial Transformer Networks

Unsupervised change detection using Spatial Transformer Netw...

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Western New York Image Processing Workshop (WNYIPW)

作者： Dan Chianucci Andreas Savakis Department of Computer Engineering Rochester Institute of Technology Rochester New York

ISBN: (纸本)9781509057672

change detection in images or video sequences is used to detect anomalies or important variations in the scene. Although much research has focused on change detection in surveillance videos, there is little work related to change detection on aerial or satellite imagery. This paper presents a deep learning approach for change detection in satellite images. The proposed approach utilizes Spatial Transformer Networks (STNs) which learn to perform a coordinate transformation to localize potential change regions. With unsupervised training the STN system examines differences between images for significant change activity. Our initial results illustrate the merit of this method.

关键词： Training change detection algorithms Neural networks Machine learning Filtering algorithms Prediction algorithms Satellites

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Data driven quickest change detection: An algorithmic complexity approach

Data driven quickest change detection: An algorithmic comple...

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IEEE International Symposium on Information Theory

作者： Husheng Li Department of Electrical Engineering and Computer Science University of Tennessee Knoxville TN

Traditional quickest change detection, which detects distribution changes in random processes, requires full or partial knowledge of pre-change or post-change distributions of samples. In practice, it is possible that the prior information is unavailable, which prohibits the direct application of existing algorithms such as the cumulative sum (CUSUM) algorithm. In this paper, data driven quickest detection is studied, with only the assumption of stationary ergodic (not necessary i.i.d. or Markovian) processes. To fully exploit existing algorithms within the probabilistic framework, the theory of algorithmic complexity (a.k.a. Kolmogorov complexity) is applied to bridge the observed samples and unknown probability distributions. In particular, data compression algorithms such as Lempel-Ziv algorithm are used to measure the unconditional and conditional algorithm complexities. Numerical simulations are carried out to demonstrate the validity of the proposed algorithms.

关键词： change detection algorithms Complexity theory Source coding Measurement Turing machines Approximation algorithms

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Forest cover change detection method using bi-temporal GF-1 multi-spectral data

Forest cover change detection method using bi-temporal GF-1 ...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Rongxin Hao Erxue Chen Zengyuan Li Institute of Forest Resources Information Technique Chinese Academy of Forestry College of Geomatics Xi'an University of Science and Technology

ISBN: (纸本)9781509033331

Forest cover will be changed by natural disaster, deforest and other activities. Remote sensing has the ability to monitor the change of forest cover in large area. However, in order to obtain the change information of forest cover, the method for getting the change information from remote sensing images is needed. China has launched GF-1 satellite in 2013, the 16m spatial resolution multi-spectral data of it should be suitable for forest cover detection applications. In this paper, I describe a set of procedures that automate forest cover change detection using a pair of GF-1 images. The proposed method was tailored to work with high spatial resolution images acquired over forest in large area. To achieve a high level of automation, automatic optimum threshold selection algorithm were applied in the processing steps. In this study, an overall change recognition accuracy of 83% has been achieved.

关键词： Remote sensing Satellites change detection algorithms Kernel Radiometry Feature extraction Signal to noise ratio

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A change detection approach of high-resolution imagery combined the pre-classification with the post-classification comparison

A change detection approach of high-resolution imagery combi...

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International Conference on Agro-Geoinformatics

作者： Ying Jiang Xiongfei Wen Daxiang Xiang Debao Tan Zhe Li Sui Zhang Youchuan Wan Spatial Information Technology Application Research Institute Changjiang River Scientific Research Institute Wuhan China School of Remote Sensing and Information Engineering Wuhan University Wuhan China

This paper presents a change detection approach for high-resolution remote sensing image combined the pre-classification with the post-classification comparison. Firstly, a spectrum correction image is constructed on the basis of fusing the differential and ratio algorithm, which can alleviate the effects of single band's sensitivity. Secondly, a binary change mask is extracted from the spectrum correction image by using Otsu. Then multiply it with the overlaying multi-temporal image to extract reliable change region. Finally, the transference process is realized by using an improved Fuzzy C-Means algorithm. Additionally, considering that Fuzzy C-Means cannot determine the cluster center and cluster number automatically, and also be sensitive to the noise, the article utilizes membership entropy to determine the optimal cluster number, and adopts the max-minimum distance to initialize the cluster center, which can avoid trapping in local optimum caused by selecting cluster center randomly, and also introduces the similarity-weight of the neighbor pixels to correct the membership function. Experiments show that the combination of pre-classification with post-classification comparison can realize the whole change process and also alleviate error propagation effects of multi-classification, which makes the change detection results closer to the real situation. Additionally, the improved Fuzzy C-Means has a better performance in determining the cluster number and center as well as reducing the noise interference.

关键词： Entropy Clustering algorithms Remote sensing Data mining Mutual information change detection algorithms Sensitivity

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Design of SAR image feature detector for small-scaled coherent change detection

Design of SAR image feature detector for small-scaled cohere...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Kee Woong Lee Hyun-Chul Kim Bumseung Kim Woo-Kyung Lee School of Electronics and Telecomm. Eng. Korea Aerospace University Goyang South Korea

Recently multi-band, high-resolution SAR images acquired from spaceborne payloads are produced on daily basis and easily available world-widely. Although the change detection techniques for SAR images have been long studied, demands for developing small-scaled change detection method algorithm are growing with the rise of SAR image features. Among others, the small-scaled change detection method is a topic of high interest in military applications for establishing future national security systems. SAR images are distinguished from optical images due to the complex nature of the inherent phase information in conjunction with amplitude parameters. The performance of detection of changes through SAR imaging are highly dependent upon both ACD(Amplitude change detection) and CCD(Coherence change detection). The ACD method, based on measurement of amplitude, is the most common approach. It simplifies the technical mechanism of imaging as it is sensitive to changes caused by artificial objects and buildings on land. The CCD method, using the information on phase, measures the extent of similarities of multi-temporal images or their coherences. In this paper, CCD algorithms are adopted to detect local changes in dense urban areas acquired from spaceborne SAR payloads. It will be further perceived to construct efficient monitoring systems on strategic target areas.

关键词： Synthetic aperture radar Coherence Charge coupled devices change detection algorithms Optical sensors Detectors Adaptive optics

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Online change detection of linear regression models

Online change detection of linear regression models

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jun Geng Bingwen Zhang Lauren M. Huie Lifeng Lai School of Elec. and Info. Engr. Harbin Iust. of Tech Harbin China Dept. of Elec. and Comp. Engr. Worcester Poly. Inst. Worcester MA Air Force Research Laboratory Rome NY

ISBN: (纸本)9781479999897

In this paper, we consider the problem of quickly detecting an abrupt change of linear coefficients in linear regression models. In particular, the observer sequentially observes a sequence of observations {(xn, yn)}∞n=1, which is assumed to obey a linear regression model at each time slot n. Some of the coefficients in the linear model change at a fixed but unknown time t. The post-change linear coefficients are unknown to the observer. The observer aims to design an online algorithm to detect the model change based on his sequential observations. Two performance metrics, namely the worst case detection delay (WADD) and the average run length to false alarm (ARL2FA), are adopted to evaluate the performance of detection algorithms. We design a low complexity algorithm, termed as parallel sum algorithm, for the detection purpose. An asymptotic upper bound on WADD is provided under any given ARL2FA constraint.

关键词： Observers Biological system modeling Algorithm design and analysis change detection algorithms Linear regression Delays Complexity theory

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