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检索条件"主题词=Change Detection Algorithms"

共 1584 条记录，以下是181-190 订阅

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

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

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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 clone selection algorithm with niching strategy inspiring by biological immune principles for change detection

A clone selection algorithm with niching strategy inspiring ...

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IEEE International Symposium on Intelligent Control (ISIC)

作者： Yajing Zhang Chaozhen Hou Department.of Automatic Control Beijing Institute of Technology Beijing China

This paper presents a clone selection algorithm with niching strategy (NCSA) for computer immune system. Based on the two bio-inspired concepts namely, negative selection and clone selection, joining mutation operator in genetic algorithms (GA) and niching strategy in biology is adopted, the number of detectors is decreased effectively and the ability on self-nonself discrimination is improved. Simulation experiment shows that the algorithm is simple, practical and adapts to the change detection for long files.

关键词： Cloning change detection algorithms Immune system Biology computing Detectors Genetic mutations Genetic algorithms Computational modeling Computer viruses Chaos

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Speaker change detection and speaker clustering using VQ distortion for broadcast news speech recognition

Speaker change detection and speaker clustering using VQ dis...

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

作者： K. Mori S. Nakagawa Department of Information and Computer Sciences Toyohashi University of Technology Toyohashi Aichi Japan

Addresses the problem of the detection of speaker changes and clustering speakers when no information is available regarding speaker classes or even the total number of classes. We assume that no previous information on speakers is available (no speaker model, no training phase) and that people do not speak simultaneously. The aim is to apply speaker grouping information to speaker adaptation for speech recognition. We use vector quantization (VQ) distortion as the criterion. A speaker model is created from successive utterances as a codebook by a VQ algorithm, and the VQ distortion is calculated between the model and an utterance. A result was obtained by the experiment on speaker detection and speaker clustering. The speaker change detection experiment was compared with results by generalized likelihood ratio and Bayesian information criterion. We show the superiority of our proposed method.

关键词： Broadcasting Loudspeakers Speech recognition Acoustic distortion Broadcast technology Magnetooptic recording Vector quantization change detection algorithms Clustering algorithms Bayesian methods

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Land Cover change detection Based on Spatial-Temporal Sub-Pixel Evolution Mapping: A Case Study for Urban Expansion

Land Cover Change Detection Based on Spatial-Temporal Sub-Pi...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Da He Yanfei Zhong Liangpei Zhang Mapping and Remote Sensing Wuhan University P. R. China Collaborative Innovation Center of Geospatial Technology Wuhan University Wuhan China

In the past decades, land cover change detection (LCCD) has been dramatically developed, since it provides corroborative support for policy decision, regulatory actions, and subsequent urban-rural activities. Satellite remote sensing image is the major source of LCCD since it is able to revisit the Earth's surface regularly and provide time series images for monitoring and space-time analysis. However, there is always a trade-off between spatial scale and temporal scale, i.e., finer spatial resolution image generally has a lower revisit frequency, leading to an observation omission; while higher revisit frequency image usually has a lower spatial resolution, resulting in a deficiency in detecting finer scale change information. In this paper, a spatial-temporal sub-pixel mapping (SSM) algorithm is proposed on the premise that one pair of fine spatial resolution image with low frequency revisit period and coarse spatial resolution with high frequently revisit period are available, and SSM is taken to restore the coarse image to a finer scale thematic map which can be then compared to the fine image, realizing a frequency and detailed LCCD. SSM is an extension of traditional mono-temporal sub-pixel mapping (SPM) algorithm, and is improved by incorporating temporally fine distribution patterns for a more appropriate restoration of coarse image. A study case for urban expansion LCCD were carried out to verify the ability of the proposed algorithm to handle change detection based on one pair of china-made Gaofen-2 image (GF-2) and Landsat-8 image, the result demonstrate that the proposed SSM algorithm outperform the other traditional SPM, achieving both fine temporal resolution and spatial resolution LCCD for further applications.

关键词： Remote sensing Spatial resolution Earth Artificial satellites Monitoring Image restoration change detection algorithms

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change detection using multiscale segmentation and Kullback-Leibler divergence: Application on road damage extraction

Change detection using multiscale segmentation and Kullback-...

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International Workshop on the Analysis of Multi-temporal Remote Sensing Images (MultiTemp)

作者： Moslem Ouled Sghaier Richard Lepage Ecole de technologie supérieure Montréal Québec Canada

This paper addresses the problem of change detection from very high resolution remotely sensed images and its application on road damage extraction in case of major disaster. The proposed methodology is based on the multiscale image segmentation using the Haar wavelet in order to define the appropriate unit of analysis for the comparison step. The Kullback-Leibler divergence is then applied as a similarity measurement to identify changed regions. This strategy is adapted to solve the road damage extraction problem by applying the Dempster-Shafer theory (DST). The images acquired during the earthquake that hits Port-au-Prince (Haiti) on 12 January 2010 are used in the experimentations and the obtained results demonstrate the accuracy and the efficiency of the described method.

关键词： Image segmentation Roads Remote sensing change detection algorithms Image edge detection Image resolution Noise

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Competitive artificial neural network for change-detection of land cover: an unsupervised approach

Competitive artificial neural network for change-detection o...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： M.L.F. Velloso M. Simoes T.A. Carneiro Department of Electronics Engineering University of State of Rio de Janeiro Rio de Janeiro Brazil Department of Computer Engineering University of State of Rio de Janeiro Rio de Janeiro Brazil

This work investigates the potential of an unsupervised network classifier, the Centroid Neural Network (CNN), for land cover change detection in remotely sensed images. Experiments carried out to evaluate the algorithm include change detection in both approaches: pre-classification and post-classification. Results confirm the effectiveness of this technique.

关键词： Artificial neural networks Cellular neural networks Clustering algorithms change detection algorithms Neural networks Remote sensing Remote monitoring Satellites Data engineering Image processing

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Non-Local Means Filtering based Landslides Coherence change detection for Multitemporal SAR Images

Non-Local Means Filtering based Landslides Coherence Change ...

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Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

作者： Zhanze Wang Peifeng Ma Institute of Space and Earth Information Science The Chinese University of Hong Kong Hong Kong China Department of Geography and Resource Management The Chinese University of Hong Kong Hong Kong China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Landslides often occur in mountains, where the low coherence characteristics result in decreased accuracy of traditional SAR-based change detection algorithms. In this paper, a non-local means filtering based coherence change detection algorithm is proposed for multitemporal SAR images. First, Persistent Scatterers (PSs) and Distributed Scatterers (DSs) are both detected to improve the monitoring density in low coherence area. Then, coherence change detection is adopted based on the multitemporal SAR images to replace traditional amplitude based change detection algorithm. Finally, non-local means filtering is used to improve the change detection accuracy of the whole area. The raw data of landslides occurred in Hong Kong is used to indicate the effectiveness of the proposed algorithm.

关键词： Landslides Accuracy Filtering change detection algorithms Urban areas Coherence Radar polarimetry Robustness detection algorithms Monitoring

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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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Unsupervised change-detection in Color Fundus Images of the Human Retina

Unsupervised Change-Detection in Color Fundus Images of the ...

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Proceedings of the Nordic Signal Processing Symposium (NORSIG)

作者： A. Nappo J. A. Benediktsson S. B. Serpico S. R. Joelsson G. Moser R. A. Karlsson G. H. Halldorsson S. H. Hardarson E. Stefansson Department of Biophysical and Electronic Engineering (DIBE) University of Genoa Genoa Italy Department of Electrical and Computer Engineering and Department of Ophthalmology University of Iceland Reykjavik Iceland

The aim of this paper is to develop an automatic method for the detection of the changes that occurred in multitemporal digital images of the fundus of the human retina, in terms of white and red spots. The images are acquired from the same patient at different times by a color fundus camera. The proposed approach is unsupervised and is based on a minimum-error thresholding technique. This technique is applied both to separate the "change" and the "no-change" classes in a suitably defined difference image, and to distinguish among different typologies of change. The algorithm is tested on 10 multitemporal pairs of images. A quantitative assessment of the change detection performances suggests that the method is able to provide accurate change maps, although possibly affected by misregistration errors or calibration/acquisition artifacts

关键词： Humans Retina change detection algorithms Diseases Retinopathy Pixel Optical sensors Optical noise Electronic mail Digital images

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