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检索条件"主题词=change detection algorithms"

共 1587 条记录，以下是461-470 订阅

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Active learning classification and change detection on multispectral images

Active learning classification and change detection on multi...

引用

IEEE International Conference on Industrial Informatics (INDIN)

作者： Kiran Mantripragada José Alberto Quintanilha Mariana Abrantes Giannotti IBM Research Brazil University of São Paulo - Polytechnic School University of São Paulo - Polytechnic School

The performance of image classification usually depends on the quality of labelled datasets to be used as training samples. In the context of remote sensing, the acquisition of ground-truth data can be a difficult and expensive task because it depends on the comprehensive surveys over the area of interest while the labelling task must be performed by experienced professionals. On the other hand, algorithms based on Active Learning can be helpful to overcome the lack of training samples. We present a cohesive algorithm for image classification and change detection based on Active Learning, that tackles the lack of ground-truth data. Afterwards, we compute the Principal Component Analysis over post-classification images to detect deforestation on the eastern side of So Paulo urban area. Our approach provides a way to automatically select data samples, while it also suggests a category. The user provides the category data (labelling task) to the selected pixels which are further used as training data in the final classification step. We applied the algorithm over four 6-channels multispectral images of the Landsat 5/TM device and we classified the pixels in two categories (“forest” and “non-forest”) for the years of 1986, 1996, 2003, and 2011. The change detection, is computed through an automatic threshold applied on the post-classification images. We were able to quantify de deforestation suffered by the eastern side of Sao Paulo city along the years. Our results show that the remaining 31% of forest in 1986 reach a minimum of 25% in 2003, but afterwards it recovered to 27% of the area in 2011.

关键词： Remote sensing Training Principal component analysis Maximum likelihood estimation Histograms change detection algorithms Monitoring

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Backscattering change detection in SAR images using wavelet techniques

Backscattering change detection in SAR images using wavelet ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Mingquan Bao Center for Remote Imaging Sensing and Processing Faculty of Science National University of Singapore Singapore

Backscattering change detection in synthetic aperture radar (SAR) is a challenging task, due to the presence of speckle noise. A 3-dimensional (2 spatial and 1 time dimension) wavelet shrinkage algorithm is developed to remove the noise. Compared to the often used 2-dimensional SAR wavelet shrinkage algorithm, this 3-dimensional algorithm exploits the correlation between SAR images, and therefore, a better signal to noise ratio is obtained. Furthermore, an autoregressive (AR) model is applied to analyze the temporal changes of radar backscattering. The change detection can be performed according to the AR model coefficients. Radarsat and ERS-2 SAR images are used to test this algorithm. The results show a significant improvement in the backscattering change detection.

关键词： Backscatter Speckle Radar detection change detection algorithms Filters Wavelet transforms Wavelet coefficients Wavelet domain Synthetic aperture radar Signal to noise ratio

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DISTRIBUTED SAR IMAGE change detection BASED ON SPARK

DISTRIBUTED SAR IMAGE CHANGE DETECTION BASED ON SPARK

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IEEE International Geoscience and Remote Sensing Symposium

作者： Huming Zhu Yuqi Guo Mingwei Niu Guodong Yang Licheng Jiao Key Lab. of Intell. Perception & Image Understanding of Minist. of Educ. Xidian Univ. Xi'an China

ISBN: (纸本)9781479979301

SAR image change detection is a fundamental process in many applications such as damage assessment, natural disasters monitoring and urban planning. Now as the scale of images and the complexity of algorithms rise, sequential methods have been more and more inefficient and powerless. In this paper, we propose a distributed parallel image change detection method based on Spark, an in-memory cluster computing framework, which provides an original support for iterative jobs. The proposed method can make full use of the power of a cluster or a set of commercial computers to process large scale SAR images. Different from the traditional image change detection, a distributed parallel kernel fuzzy c-means clustering algorithm, which is integrated with Spark, is used to part the change map into changed area and unchanged area. Our experimental results on some large scale SAR images show good effectiveness and accelerating performance. Compared to Hadoop based KFCM, the speedup can achieve 18.9 in maximum.

关键词： fuzzy set theory iterative methods parallel algorithms pattern clustering radar imaging sequential estimation synthetic aperture radar Hadoop based KFCM Spark commercial computers damage assessment distributed SAR image change detection distributed parallel image change detection method distributed parallel kernel fuzzy c-means clustering algorithm in-memory cluster computing framework iterative jobs large scale SAR image change detection natural disaster monitoring sequential methods unchanged area urban planning Algorithm design and analysis change detection algorithms Clustering algorithms Computational modeling Kernel Sparks Synthetic aperture radar KFCM Synthetic aperture radar(SAR) change detection Synthetic aperture radar Image change detection Algorithm design and analysis damage assessment Clustering algorithms sequential estimation City Planning sparks Radar imaging Parallel algorithms Fuzzy Set Theory Computational modeling iterative methods Cluster Analysis change detection algorithms

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An Efficient Video De-interlacing with Scene change detection

An Efficient Video De-interlacing with Scene Change Detectio...

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International Conference on Information, Communications and Signal Processing

作者： Chung-chi Lin Ming-hwa Sheu Huann-keng Chiang Chishyan Liaw Ching-tsorng Tsai Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Science and Information Engineering Tunghai University Taichung Taiwan

An efficient video de-interlacing technique with scene change detection is proposed and its performances are examined. Scene changes happen quite often in film broadcasting and they tend to destabilize the quality of performance such as jagged effect, blurred effect, and artifacts effect, while de-interlacing technique is utilized. Therefore, the issue of scene change needs to be addressed with de-interlacing process. In the proposed method, de-interlacing begins with scene change detection, which is to ensure that the interfield information is used correctly. To improve the quality of de-interlacing, the factors of scene change are taken into account when de-interlacing techniques are applied. The simulation results show that the proposed algorithm exhibits better performances than other interpolation algorithms

关键词： Layout Computer displays Liquid crystal displays Interpolation Multimedia communication Broadcasting change detection algorithms Video sequences Adaptive filters Microcomputers

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A Color-Based Gray-Level Compensation Algorithm for Fast change detection

A Color-Based Gray-Level Compensation Algorithm for Fast Cha...

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

作者： Chang Su Aishy Amer Electrical and Computer Engineering Concordia University Montreal QUE Canada

Gray-level based change detection is effective but suffers when gray-levels of objects are similar to backgrounds. A color-based gray-level compensation algorithm for fast change detection is proposed in this paper. Based on the object scatter estimation in difference frames, the gray-levels of the non-significant pixels in object regions are compensated according to their significance probabilities in color channels. Experimental results show that the proposed method significantly improve the quality of difference frames.

关键词： change detection algorithms Colored noise Object detection Scattering Testing Image segmentation Video compression Image processing Image color analysis Euclidean distance

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Regularized logistic regression method for change detection in multispectral data via Pathwise Coordinate optimization

Regularized logistic regression method for change detection ...

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IEEE International Conference on Image Processing

作者： Jiming Li Yuntao Qian Sen Jia College of Computer Science Zhejiang University Hangzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Remotely sensed data by sensors on satellite or airborne platform, is becoming more and more important in monitoring the local, regional and global resources and environment. In this paper, we utilize the regularized logistic regression model for change detection of large scale remotely sensed bi-temporal multispectral images. change detection methods base on classification schemes under this kind of condition should put more emphasis on the model's simplicity and efficiency in addition to the detection accuracy. The simple linear classifier is solved by recent proposed “Pathwise Coordinate Descent”. When applied on the L1-regularized regression problem, the algorithm can handle large problems in a comparatively very low timing cost. Through computing the solutions for a decreasing sequence of regularization parameters, the algorithm also combines model selection procedure into itself. We experiment the logistic regression with elastic-net convex penalty. Experimental results from a real data set demonstrate that, models obtained by Pathwise Coordinate Descent algorithm only need very low computational costs. The achieved remarkable efficiency indicates that regularized logistic regression via Pathwise Coordinate Descent is a promising method for large scale change detection problem in remote sensing.

关键词： Support vector machines Computational modeling Logistics Remote sensing change detection algorithms Accuracy Timing

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Efficient video object segmentation using adaptive background registration and edge-based change detection techniques

Efficient video object segmentation using adaptive backgroun...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Jiashu Zhang L. Zhang Heng-Ming Tai Sichuan Province Key Laboratory of Signal and Information Processing Southwest Jiaotong University Chengdu China Department of Electrical Engineering University of Tulsa Tulsa OK USA

The paper presents an automatic and efficient video object segmentation algorithm. Moving object extraction is carried out by adaptive background model and edge-based change detection techniques. The background is updated by use of pixel history and a moving object mask. Connected component analysis and morphological filtering are employed to obtain an accurate VOP (video object plane). Finally, a novel object tracking window scheme is applied to improve the processing speed. Experimental results for three different types of MPEG-4 video sequences are shown to demonstrate the effectiveness of the proposed algorithm

关键词： Object segmentation Object detection Video sequences change detection algorithms Adaptive signal detection MPEG 4 Standard Data mining Shape Motion detection Image edge detection

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detection of Abrupt change in Channel Covariance Matrix for Multi-Antenna Communication

Detection of Abrupt Change in Channel Covariance Matrix for ...

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2021 IEEE Global Communications Conference (GLOBECOM)

作者： Runnan Liu Liang Liu Dazhi He Wenjun Zhang Erik G. Larsson SEIEE Shanghai Jiao Tong University The Hong Kong Polytechnic University Linköping University

ISBN: (纸本)9781728181059

The knowledge of channel covariance matrices is of paramount importance to the estimation of instantaneous channels and the design of beamforming vectors in multi-antenna systems. In practice, an abrupt change in channel covariance matrices may occur due to the change in the environment and the user location. Although several works have proposed efficient algorithms to estimate the channel covariance matrices after any change occurs, how to detect such a change accurately and quickly is still an open problem in the literature. In this paper, we focus on channel covariance change detection between a multi-antenna base station (BS) and a single-antenna user equipment (UE). To provide theoretical performance limit, we first propose a genie-aided change detector based on the log-likelihood ratio (LLR) test assuming the channel covariance matrix after change is known, and characterize the corresponding missed detection and false alarm probabilities. Then, this paper considers the practical case where the channel covariance matrix after change is unknown. The maximum likelihood (ML) estimation technique is used to predict the covariance matrix based on the received pilot signals over a certain number of coherence blocks, building upon which the LLR-based change detector is employed. Numerical results show that our proposed scheme can detect the change with low error probability even when the number of channel samples is small such that the estimation of the covariance matrix is not that accurate. This result verifies the possibility to detect the channel covariance change both accurately and quickly in practice.

关键词： Maximum likelihood estimation Maximum likelihood detection Error probability change detection algorithms Conferences Channel estimation Detectors

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A New Vehicle detection with Distance Estimation for Lane change Warning Systems

A New Vehicle Detection with Distance Estimation for Lane Ch...

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IEEE Symposium on Intelligent Vehicle

作者： Bing-Fei Wu Wei-Hsin Chen Chai-Wei Chang Chao-Jung Chen Ming-Wei Chung Department of Electrical and Control Engineering National Chiao Tung University Hsinchu Taiwan

In this paper, the vehicle detection with distance estimation algorithm is proposed by using two CCD cameras mounted on the both side mirrors of our experimental car, TAIWAN iTS-1. The Sobel edge pixels and gray intensity are applied for the lane marking and vehicle detection in our work. A new detection which utilizes a lane prediction technique to reduce the size of the ROI is developed for real-time embedded system applications. The side vehicles can be easily detected by comparing the gray intensity with the road surface. Then, the image coordinate model is utilized for the distance estimation with the detected approaching vehicles. The verified effective distance is 30 m behind the host vehicle. This approach has been successfully implemented on the DSP board for the real-time detection and this system will be extended to the lane change warning system to give drivers alerts to avoid collision.

关键词： Vehicle detection Alarm systems Real time systems change detection algorithms Charge coupled devices Charge-coupled image sensors Mirrors Embedded system Road vehicles Digital signal processing

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ISANet: An Interactive Self-attention Network for Cropland Image change detection

ISANet: An Interactive Self-attention Network for Cropland I...

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International Conference on Control and Automation (ICCA)

作者： Sijun Dong Yanrui Chen Fann Wu Zhi Gao Xiaoliang Meng School of Remote Sensing and Information Engineering Wuhan University Wuhan China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

The change detection task is a vital research topic in the remote sensing area, particularly significant in the context of agricultural land monitoring. Currently, many researchers perform change detection tasks based on the self-attention mechanism. A common method involves applying a self-attention mechanism within single-temporal images, then fusing the feature maps of bitemporal images to decode and obtain the prediction result. We propose a bitemporal interactive self-attention (ISA) mechanism for change detection tasks, with a special focus on cropland areas where monitoring changes can lead to enhanced agricultural productivity and resource management. In the ISA module, we construct pixel dependency relationships both within single-temporal images and between bitemporal images. This allows the network to learn the feature relationship between bitemporal images through the ISA module, thus optimizing the effect of the change detection task. Moreover, we propose a hybrid convolutional feature fusion module that strengthens the learning of change features in the decoding part. Ultimately, the ISANet network we proposed surpassed other advanced algorithms on the Cropland change detection (CLCD) dataset. The experimental results show that the proposed ISANet network can deliver robust performance in remote sensing cropland image change detection tasks.

关键词： Productivity Convolution change detection algorithms Feature extraction Decoding Resource management Task analysis

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