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检索条件"主题词=change detection algorithms"

共 1587 条记录，以下是471-480 订阅

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Retrieve seismic damages from remote sensing images by change detection algorithm

Retrieve seismic damages from remote sensing images by chang...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： A.X. Dou J.F. Zhang Y.F. Tian Institute of Crustal Dynamics China Seismological Bureau Beijing China

This paper addresses the problem of seismic damage based on the remote sensing image change detection. Comparison technique has long been recognized as an accurate model to describe a variety of image characteristics. Here, we develop algorithms, subtraction and ratio based on a disjointed pixel, correlativity and comparability based on neighboring pixels, respectively. By choosing the suitable threshold to segment differences in resulting images, we perform the post-earthquake damage assessment. Finally, the high resolution images acquired pre-and post-earthquake are presented for illustration and performance evaluation, and satisfied result are gained.

关键词： Image retrieval Remote sensing detection algorithms Pixel Earthquakes change detection algorithms Seismology Character recognition Image recognition Image segmentation

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Feature Augmentation Improves Anomalous change detection for Human Activity Identification in Synthetic Aperture Radar Imagery

Feature Augmentation Improves Anomalous Change Detection for...

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IEEE Southwest Symposium on Image Analysis and Interpretation

作者： Hannah J. Murphy Christopher X. Ren Matthew T. Calef New Mexico Institute of Mining and Technology Socorro NM USA Intelligence and Space Research Division Los Alamos National Laboratory Los Alamos NM USA Descartes Labs Inc. Santa Fe NM USA

ISBN: (数字)9781728157450

ISBN: (纸本)9781728157467

Anomalous change detection (ACD) methods separate common, uninteresting changes from rare, significant changes in co-registered images collected at different points in time. In this paper we evaluate methods to improve the performance of ACD in detecting human activity in SAR imagery using outdoor music festivals as a target. Our results show that the low dimensionality of SAR data leads to poor performance of ACD when compared to simpler methods such as image differencing, but augmenting the dimensionality of our input feature space by incorporating local spatial information leads to enhanced performance.

关键词： Synthetic aperture radar Optical imaging Optical sensors Backscatter change detection algorithms Encyclopedias

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HGS based CWNN- Hunger Game Search based Convolutional Wavelet Neural Network for Climate change detection

HGS based CWNN- Hunger Game Search based Convolutional Wavel...

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Trends in Engineering Systems and Technologies (ICTEST), International Conference on

作者： Aiswarya Jeevan S Amala Shanthi Department of Electronics and Communication Engineering Noorul Islam Centre For Higher Education Tamil Nadu India

ISBN: (数字)9798350307795

ISBN: (纸本)9798350307801

Climate change is a critical issue that requires effective monitoring and timely intervention. Remote sensing techniques and machine learning algorithms have shown potential for detecting climate change, and several models have been proposed in recent years. The research contributes towards enhancing climate change detection capabilities, which are vital for addressing environmental challenges and understanding the impacts of climate change. This study introduces a new strategy for climate change detection according to Hunger Game Search (HGS) algorithm and Convolutional Wavelet Neural Network (CWNN), named HGS-based CWNN. CWNN algorithm has been shown to perform well in a variety of classification tasks, achieving high levels of accuracy. Also, HGS algorithm is highly explorative, which means that it is capable of finding solutions in an efficient manner, and does not get stuck in local optima. Thus, training CWNN using HGS algorithm can improve the performance further. The method is evaluated on two benchmark datasets, and the findings display its effectiveness in detecting climate change patterns with higher accuracy rate of 0.976% and lower MSE rate of 0.023. In general, the evaluation found that the suggested model displayed superior performance compared to established models in terms of precision and mean squared error (MSE).

关键词： Training Climate change Analytical models Accuracy change detection algorithms Neural networks Games Convolutional neural networks Environmental monitoring Search methods Metaheuristics Algorithm design and theory Optimization methods

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A Novel Sliding Window Based change detection Algorithm for Asymmetric Traffic

A Novel Sliding Window Based Change Detection Algorithm for ...

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IFIP International Conference on Network and Parallel Computing (NPC)

作者： Ejaz Ahmed Andrew Clark George Mohay Queensland University of Technology Brisbane Australia

The effects of network attacks may result in abrupt changes in network traffic parameters. The speedy identification of these changes is critical for smooth network operation. This paper illustrates a sequential analysis technique for detecting these unknown abrupt changes in asymmetric network traffic. A novel sliding window based adaptive cumulative sum (CUSUM) algorithm is used to detect the cause of such variations in network traffic. The significance of the proposed algorithm is two-fold: (1) automatic adjustment of the change detection threshold while minimising the false alarm rate, and (2) timely detection of an end to the anomalous traffic. The validity of the proposed technique is investigated by experimentation on simulated data and on 18 months of real network traces collected from a class C darknet. Comparative analysis of the proposed technique with a traditional CUSUM method demonstrates its superior performance with high detection accuracy and low false alarm rate.

关键词： detection algorithms Telecommunication traffic change detection algorithms Monitoring IP networks Computer crime Algorithm design and analysis Testing Sequential analysis Traffic control

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Online Action Representation using change detection and Symbolic Programming

Online Action Representation using Change Detection and Symb...

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IEEE International Workshop on Medical Measurement and Applications (MEMEA)

作者： Vishnu S Nair Sneha Sree Gunjan Singh Jayaraj Joseph Mohanasankar Sivaprakasam Department of Electrical Engineering Indian Institute of Technology Madras

ISBN: (数字)9798350307993

ISBN: (纸本)9798350308006

This paper addresses the critical need for online action representation, which is essential for various applications like rehabilitation, surveillance, etc. The task can be defined as representation of actions as soon as they happen in a streaming video without access to video frames in the future. Most of the existing methods use predefined window sizes for video segments, which is a restrictive assumption on the dynamics. The proposed method employs a change detection algorithm to automatically segment action sequences, which form meaningful sub-actions and subsequently fit symbolic generative motion programs to the clipped segments. We determine the start time and end time of segments using change detection followed by a piece-wise linear fit algorithm on joint angle and bone length sequences. Domain-specific symbolic primitives are fit to pose keypoint trajectories of those extracted segments in order to obtain a higher level semantic representation. Since this representation is part-based, it is complementary to the compositional nature of human actions, i.e., a complex activity can be broken down into elementary sub-actions. We show the effectiveness of this representation in the downstream task of class agnostic repetition detection. We propose a repetition counting algorithm based on consecutive similarity matching of primitives, which can do online repetition counting. We also compare the results with a similar but offline repetition counting algorithm. The results of the experiments demonstrate that, despite operating online, the proposed method performs better or on par with the existing method.

关键词： change detection algorithms Motion segmentation Surveillance Fitting Semantics Streaming media Programming

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Unsupervised change detection of Remotely Sensed Images Using Fuzzy Clustering

Unsupervised Change Detection of Remotely Sensed Images Usin...

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International Conference on Advances in Pattern Recognition (ICAPR)

作者： Susmita Ghosh Niladri Shekhar Mishra Ashish Ghosh Department of Computer Science and Engineering Jadavpur University Kolkata India Department of Electronics and Communication Engineering Netaji Subhash Engineering College Kolkata India Machine Intelligence Unit and Center of Soft Computing Research Indian Statistical Institute Kolkata India

In this paper two fuzzy clustering algorithms, namely fuzzy C-means (FCM) and Gustafson Kessel clustering (GKC), have been used for detecting changes in multitemporal remote sensing images. change detection maps are obtained by separating the pixel-patterns of the difference image into two groups. To show the effectiveness of the proposed technique, experiments are conducted on three multispectral and multitemporal images. Results are compared with those of existing Markov random field (MRF) & neural network based algorithms and found to be superior. The proposed technique is less time-consuming and unlike MRF do not need any a priori knowledge of distribution of changed and unchanged pixels (as required by MRF).

关键词： Clustering algorithms Pixel change detection algorithms Remote sensing Neural networks Object detection Remote monitoring Statistical distributions Context modeling Arithmetic

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change detection of Images Based on Multivariate Alteration detection Method

Change Detection of Images Based on Multivariate Alteration ...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Jeberson Retna Raj Senduru Srinivasulu School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9781728151977

ISBN: (纸本)9781728151984

Advancement of Satellite technology gives rapid response to the complex problems and decision making. Due to the impact of climate change, loss of rainfall, drought, risen of sea level are some of the consequences. Recently, the cyclone Fani hits oddisa severely and the livelihood of the people is in standstill. The satellite images of before and after Fani shows the severity of the cyclone. This paper, we introduced three methods which includes Large Scale Mean Shift (LSMS), Multivariate Alteration detection (MAD) and Local statistic Feature extraction methods are used to detect the changes in the heterogeneous images. The satellite images of before and after the cyclone Fani is considered for processing and the severity is estimated. The satellite images are calibrated and preprocessed for removing the speckle noises. With the application of regression prediction algorithm used to tune the accuracy of segmentation and change detection process. The model is tested with the satellite image and the results are very promising. This approach can also be applied in medical images to detect the changes in features, which helps the physician to diagnose the decease.

关键词： Satellites Image segmentation Feature extraction change detection algorithms Prediction algorithms Cyclones Speckle

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Model-based feature classification and change detection

Model-based feature classification and change detection

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： F.M. Ngai J.C. Curlander A.D. Stocker Vexcel Corporation Boulder CO USA Space Computer Corporation Santa Monica CA USA

Describes a novel image processing system which performs constant false-alarm rate (CFAR) change detection and model-based feature classification on temporally separated and precision registered images. The authors present results of algorithms performed on 0.5 m to 2 m resolution scanned aerial photographs of the Yuma, Arizona region to identify changes that correspond to man-made targets and new roads and to classify false changes.< >

关键词： Image resolution change detection algorithms Image processing Pixel Roads Spatial resolution Layout Gas detectors Computer vision Data processing

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An object-based change detection approach by integrating intensity and texture differences

An object-based change detection approach by integrating int...

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International Asia Conference on Informatics in Control, Automation and Robotics, CAR

作者： Lingcao Huang Guo Zhang Yang Li School of Remote Sensing Information Engineering Wuhan University of China Wuhan China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University of China Wuhan China Chinese Academy of Surveying and Mapping Beijing China

ISBN: (纸本)9781424451920;9781424451937;9781424451944

With the development of remote sensing technology, using remote sensing images to do change detection is becoming a hotspot. This paper introduces the research status of pixel-based and object-based change detection technology as well as comments on several change detection methods. A technique for pixel-based change detection by integrating the intensity and texture differences between two frames is studied. An object-based change detection method based on this technique is proposed. Experimental results show that the integrated measure is robust with respect to the illumination changes and noise, the performance of which is outstanding especially when applied to object-based change detection because of pixels' consistency in an object and sharp contrast between objects.

关键词： Remote sensing Pixel Object detection Image analysis Earth Humans Algorithm design and analysis Robotics and automation change detection algorithms detection algorithms

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Robust change detection in High Resolution Satellite Images with Geometric Distortions

Robust Change Detection in High Resolution Satellite Images ...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Dongkwon Jin Kyungsun Lim Chang-Su Kim School of Electrical Engineering Korea University Seoul Korea

ISBN: (数字)9781728132488

ISBN: (纸本)9781728132495

Ahstract-A robust change detection algorithm for high resolution satellite images, which are not perfectly registered, is proposed in this work. To achieve this goal, a change detection technique for registered images and an image registration technique are employed in a cooperative way. Specifically, we use not only hand-crafted features but also change detection results to match keypoints extracted from two images. We then align the images using the matching pairs of keypoints. Finally, we obtain a change map from the aligned images. These steps of image registration and change detection are alternately iterated until the convergence. Experimental results demonstrate that proposed algorithm outperforms the conventional change detection technique significantly, when there are geometric distortions between temporal satellite images.

关键词： Satellites Feature extraction change detection algorithms Distortion Image registration Robustness

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