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检索条件"主题词=change detection algorithms"

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Normalization of Multi-Temporal Images Using a New change detection Method Based on the Spectral Classifier

Normalization of Multi-Temporal Images Using a New Change De...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： O. de Carvalho Jr. R. Guimaraes R. Gomes A. de Carvalho N. da Silva Departamento de Geografia Universidade da Brasilia Distrito Federal Brazil Departamento de Sistemas de Informação Centro Universitário de Anápolis Unievangélica Anapolis Brazil

Orbital images are difficult to maintain a radiometric precision due to the sensor oscillation, atmosphere interferences, season variation of the solar illumination angle, among others. Thus, many radiometric correction techniques have been developed for time series considering mainly: (a) landscape elements whose reflectances are nearly constant over time called of invariants features and (b) linear regression over invariants features assumes that the pixels sampled in the same places at different times are linearly correlated. Therefore, the key problem to the image regression method is an accurate selection of invariant features. In this paper is proposed new radiometric normalization software developed in Turbo C language that searches the highest quality of the invariant features. The algorithm comprises the following steps: (a) identification of the invariant points using a new change detection method based on the spectral classifier algorithms and (b) regression linear between temporal band pairs eliminating the outliers. Initially the algorithm identifies invariants points using a new change detection method based on the spectral classifier algorithms: Spectral Angle Mapper (SAM) and Spectral Correlation Mapper (SCM). In particular, this method approach allows the automatic identification of the invariants points to calibrate remote-sensing images, without visual interpretation data. Program's users establish the spectral change detection method (SAM or SCM) and the threshold value. The second step is to apply two successive linear regressions. First linear regression searches the outlier points using only the pixels with more value than threshold. The outlier points identification use root means square (RMS) and these not include in the second linear regression. Thus, this is last line regression considers only the best spectral for radiometric adjustment. Finally, the gain and offset values are determined and applied for each band in t2 image. In the case

关键词： Linear regression Radiometry change detection algorithms Image sensors Atmosphere Interference Lighting Software quality Remote sensing Root mean square

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Subpixel change detection Based on Abundance and Slope Features

Subpixel Change Detection Based on Abundance and Slope Featu...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： C.-C. Hsieh P.-F. Hsieh C.-W. Lin Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

Most of change detection algorithms for multi-temporal images are performed in unit of pixels. Due to the limit of spatial resolution, a pixel is, in many cases, a mixed pixel that contains more than one ground cover types. In order to explore more information from images, we have reviewed several spectral unmixing algorithms and used them to accomplish subpixel change detection. In an application of landslide monitoring, we demonstrated the use of subpixel change detection for detection of landslide spreading. We used spectral unmixing algorithms to extract the abundance information from multispectral images. For the particular characteristic of landslides, we incorporated the slope feature into process. Our preliminary result shows that the subpixel change detection method can provide more detailed information about landslide spread than pixel-based change detection algorithms.

关键词： Terrain factors change detection algorithms Vegetation mapping Soil Remote sensing detection algorithms Pixel Matrix decomposition Spatial resolution Data mining

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

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

引用

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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Unsupervised change-detection from Multi-Channel SAR Data

Unsupervised Change-Detection from Multi-Channel SAR Data

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Proceedings of the Nordic Signal Processing Symposium (NORSIG)

作者： Gabriele Moser Sebastiano B. Serpico Department of Biophysical and Electronic Engineering (DIBE) University of Genoa Genoa Italy

Synthetic aperture radar (SAR) data presents a great potential for environmental monitoring applications and natural disaster management thanks to their insensitivity to atmospheric and Sun-illumination conditions. However, the automatic generation of change maps from multichannel SAR images acquired on the same geographic area at different times is still an open issue in the remote-sensing literature. In the present paper an automatic unsupervised contextual change-detection method is proposed for two-date multichannel SAR images, by integrating a SAR-specific extension of the Fisher transform with the expectation-maximization (EM) algorithm or with some of its variants (the Landgrebe-Jackson EM and the stochastic EM algorithms), applied according to a Markov random field model for the image data. The method is validated by experiments on SIR-C/XSAR data

关键词： Synthetic aperture radar change detection algorithms Condition monitoring Remote monitoring Iterative algorithms Stochastic processes Markov random fields Context modeling Speckle Testing

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Automatic signal segmentation based on abrupt change detection for power systems applications

Automatic signal segmentation based on abrupt change detecti...

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IEEE Power India Conference

作者： A. Ukil R. Zivanovic Tshwane University of Technology TUT Pretoria South Africa

detection of abrupt changes in the signal parameters can be used to segment the signal. This has been particularly useful in automatic disturbance recognition in power systems, where the signals are first segmented event-specifically and processed subsequently to recognize and analyze the disturbances. This paper describes several abrupt change detection-based segmentation algorithms and their applications in the power systems. The applications include automatic disturbance recognition, synchronization, relay performance analysis etc, where recorded signals from the power network in South Africa have been used

关键词： Power systems Power system analysis computing Africa change detection algorithms Circuit faults Power system relaying Signal analysis Algorithm design and analysis Signal processing Power system modeling

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Lane change Maneuver detection from Probe Vehicle DGPS Data

Lane Change Maneuver Detection from Probe Vehicle DGPS Data

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International Conference on Intelligent Transportation

作者： Yiguang Xuan B. Coifman Department of Electrical and Computer Engineering Ohio State Uinversity Columbus OH USA Department of Civil and Environmental Engineering and Geodetic Science and Department of Electrical and Computer Engineering Ohio State Uinversity Columbus OH USA

The impact of lane change maneuvers is fundamental to microscopic traffic flow theory. Due to the difficulty of tracking many vehicles over time and space, most of the published research in this area seek to find lane change maneuvers visually from wayside cameras. This paper presents a different approach, finding the lane change maneuvers of a probe vehicle itself using differential Global Positioning System (DGPS) data. We first use multiple probe vehicle trajectories through a study corridor to establish a reference trajectory from the median of all trajectories and this reference trajectory is used to define the position of the current lane. This approach eliminates the need for high-resolution maps accurate enough to capture the exact position of the individual lanes. Our lane change maneuver detection is then divided into two parts, controlling for the impacts of mandatory lane change maneuvers (MLC) and then for discretionary lane change maneuvers (DLC). MLC are detected by comparing the difference between the mean and median of lateral distance of all trajectories relative to a reference trajectory. After distinguishing all the MLCs, the DLC are found by setting lateral thresholds around the reference trajectory, i.e., when a given trajectory leaves this virtual lane. In the process we control for the impacts of GPS errors, such as multipath, arising from obstructions. DLC are then found by comparing the out-of-threshold-line time and length to a threshold acquired empirically from data

关键词： Vehicle detection Probes Global Positioning System Vehicles Vehicle safety Radar tracking Microscopy Traffic control Image processing change detection algorithms

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Image change detection Algorithm Based on Clustering Characteristic of 2-D Histogram

Image Change Detection Algorithm Based on Clustering Charact...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： J. Zhang W. Sun W. Tang Department of Information Engineering Harbin Institute of Technology Harbin China Department of Optical-electronic Information Engineering Harbin Institute of Technology Harbin China

In this paper, a novel image change detection algorithm based on clustering characteristic of 2-D histogram formed by pixel gray levels and the local average gray levels is proposed. First, the 2-D histogram is segmented into two initial clusters representing change region and unchanged region respectively by using classical segmentation method. Then, the traditional 2-D maximum entropy principle is improved properly to adjust the initial clusters. Finally, changes are detected according to the two relative more accurate clusters that have been adjusted. Theoretical analysis and experimental results show that the proposed algorithm has more accurate detection precision, stronger anti-noise capability and faster computation than traditional 2-D maximum entropy algorithm.

关键词： detection algorithms Histograms Pixel change detection algorithms Entropy Image segmentation Optical noise Image analysis Clustering algorithms Condition monitoring

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change detection based on region likelihood ratio in multitemporal SAR images

Change detection based on region likelihood ratio in multite...

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International Conference on Signal Processing Proceedings (ICSP)

作者： Yong-min Shuai Xin Xu Hong Sun Ge Xu Laboratory of Signal Processing Electronic Information School Wuhan University of China Wuhan China

In this paper, a novel approach for change detection in multitemporal synthetic aperture radar (SAR) images is presented. The proposed approach based on region likelihood ratio feature detection exploits an edge fusion technique for the SAR images segmentation. Segmentation is the key process in change detection based on region feature and the proposed edge fusion technique of two segmentation images ensures the accuracy of the changed parts segmentation. The change-detection process is divided into three main steps: 1 ) two SAR images segmentation based on watershed algorithm; 2) edge fusion of two segmentation images; 3) the region likelihood ratio feature extraction for changed region detection on the fusion result. Experimental results obtained on real SAR images acquired by the ERS-1 confirm the effectiveness of the proposed approach

关键词： Image segmentation Image edge detection Radar detection change detection algorithms Optical sensors Feature extraction Pixel Speckle Synthetic aperture radar Signal processing algorithms

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Robust Unsupervised change detection with Markov Random Fields

Robust Unsupervised Change Detection with Markov Random Fiel...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： F. Melgani Y. Bazi Department of Information and Communication Technologies University of Trento Trento Italy

Because of the strong statistical variability of remote sensing images, the selection of the best thresholding algorithm to detect changes between two successive temporal images of the same study area without any prior knowledge is often not easy. In this paper, we face this problem through a new robust change detection approach. In order to achieve robustness, the proposed unsupervised approach is based on a Markov random field (MRF) fusion of change maps provided by an ensemble of different thresholding algorithms. Experimental results obtained on three images acquired by different sensors and referring to different kinds of changes confirm the robustness of the proposed approach.

关键词： Robustness Markov random fields change detection algorithms Face detection Remote monitoring Pixel Communications technology Remote sensing Image sensors Crops

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A Novel Theoretical Framework for Unsupervised change detection Based on CVA in Polar Domain

A Novel Theoretical Framework for Unsupervised Change Detect...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： F. Bovolo L. Bruzzone Department of Information and Communication Technologies Trento Italy

In this paper a framework for a formal definition and a theoretical study of the change vector analysis (CVA) technique is proposed. This framework, which is based on the representation of the CVA in polar coordinates, aims at: (i) introducing formal definitions in the polar domain for a better general description and understanding of the information present in spectral change vectors; (ii) analyzing from a theoretical point of view the distributions of changed and unchanged pixels in the polar domain; (iii) driving the implementation of proper preprocessing procedures for multitemporal images on the basis of the results of the theoretical study on the distributions; and (iv) defining a solid background for the development of advanced and accurate automatic change-detection algorithms in the polar domain. The findings derived from the theoretical analysis on the statistical models of classes have been validated on real multispectral and multitemporal remote sensing images.

关键词： Image analysis Information analysis Remote sensing Multispectral imaging Solids change detection algorithms Algorithm design and analysis Pixel Random variables Satellites

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