In order to reduce the influence of noise and obtain better changedetection effect, this paper proposes a method for SAR image changedetection based on mean shift pre-classification and fuzzy C-means. First, the ori...
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In order to reduce the influence of noise and obtain better changedetection effect, this paper proposes a method for SAR image changedetection based on mean shift pre-classification and fuzzy C-means. First, the original image is pre-classified based on mean shift clustering. As a clustering method with non-parametric density estimation, mean shift can effectively maintain the edge information of the object, and can smooth the pixel intensity of the same type of object to reduce the influence of noise on changedetection. Then, the difference map is generated by the log-ratio operator and classified into changed area, uncertain area, and unchanged area. After the adjustment, the pre-classification is performed by mean shift and the difference map is generated. Finally, the improved FCM algorithm is used to classify the difference map to generate changedetection result map. The effectiveness of the proposed method is verified by experiments with different contrast algorithms on real SAR image datasets.
This paper proposes a DCT-based moving object extraction algorithm which is focused on rainy condition by changedetection method. The DCT technique is used to decrease the rainy effect in order to construct a reliabl...
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This paper proposes a DCT-based moving object extraction algorithm which is focused on rainy condition by changedetection method. The DCT technique is used to decrease the rainy effect in order to construct a reliable background model. Then, we use changedetection to classify pixels in video frame into the foreground region or background region so as to acquire an initial object mask. Besides, reflection effect can be removed from, initial object mask through edge detection and bounding box match. Finally, a post-processing is used to refine the boundary of moving object by connect component labeling and morphological operation. Experimental results show that the proposed algorithm can provide a satisfactory segmentation result in rainy situations
Video is an important and challenging medium and requires sophisticated indexing schemes for efficient retrieval from visual databases. An important step in video indexing is scene changedetection. Recently, several ...
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Video is an important and challenging medium and requires sophisticated indexing schemes for efficient retrieval from visual databases. An important step in video indexing is scene changedetection. Recently, several scene change detection algorithms in the pixel and compressed (MPEG-2) domains have been reported in the literature. These algorithms are computationally complex and are not very robust in detecting gradual scene changes. The authors propose an efficient technique for detecting scene changes in the MPEG-2 compressed domain. The proposed algorithm has the advantage of fast scene changedetection. In addition, this algorithm has the potential to detect gradual scene changes.
Detecting landscape changes using very high-resolution multispectral imagery demands an accurate and scalable algorithm that is robust to geometric and atmospheric errors. Existing pixel-based changedetection approac...
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Detecting landscape changes using very high-resolution multispectral imagery demands an accurate and scalable algorithm that is robust to geometric and atmospheric errors. Existing pixel-based changedetection approaches, however, have several drawbacks, which render them ineffective for VHR imagery analysis. A recent probabilistic changedetection framework provides more accurate assessment of changes than traditional approaches by analyzing image patches than pixels. However, this patch (grid)-based approach produces coarse-resolution (patch size) changes. In this work we present a sliding window based approach that produces changes at the native image resolution. The increased computational demand of the sliding window based approach is addressed through thread-level parallelization on shared memory architectures. Our experimental evaluation showed a 91% performance improvement compared to its sequential counterpart on a sq. KM aerial image with varying window sizes on a 16-core (32 virtual threads) Intel Xeon processor.
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 correcti...
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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 changedetection 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 changedetection 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 changedetection 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
This work considers the problem of changedetection of working regimes from industrial processes, e.g. electric machines with rotation elements, and which generates mechanical vibrations. Two approaches are considered...
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This work considers the problem of changedetection of working regimes from industrial processes, e.g. electric machines with rotation elements, and which generates mechanical vibrations. Two approaches are considered: (i) based on signal processing and pattern recognition methods; (ii) based on sparse methods. The objective of the paper is to evaluate the preliminary results obtained by the above approaches and to promote methods based on sparse representations and computations for changedetection problems, as alternative to classical methods based on transform or pattern recognition. The results are encouraging and suggest that more studies on the method of sparse computation as an optimal candidate for changedetection from time detection point of view is needed.
This paper presents a new technique for high resolution enhancement of Remote Sensing (RS) imagery degraded in a random propagation channel and contaminated with composite noise (additive and multiplicative). The prop...
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This paper presents a new technique for high resolution enhancement of Remote Sensing (RS) imagery degraded in a random propagation channel and contaminated with composite noise (additive and multiplicative). The proposed method aggregates two Neural Network (NN) paradigms: The Modified Hopfield Neural Network (MHNN) and The Pulse Coupled Neural Network (PCNN). In the fused strategy, we propose the MHNN technique with the objective to provide the enhanced RS image reconstruction followed by the PCNN algorithm that performs precise changedetection. We apply the PCNN for the target detection, segmentation and classification in the reconstructed RS image. Computer simulations examples are reported to illustrate the usefulness of the aggregated unified PCNN-MHNN technique for enhance changedetection.
To deal with the problem that traditional satellite remote sensing image changedetection methods overestimate changed areas, a context-sensitive similarity based supervised satellite image changedetection method was...
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ISBN:
(纸本)9781509040940
To deal with the problem that traditional satellite remote sensing image changedetection methods overestimate changed areas, a context-sensitive similarity based supervised satellite image changedetection method was proposed. Both context-sensitive magnitude and direction of change in the vicinity of each pixel by means of local intercept and slope were exploited, and then SVM (support vector machine) with local intercept and slope was used in satellite image changedetection. In the experiment for changedetection of high resolution bi-temporal multispectral earthquake satellite images including building damage, the results showed that compared to standard SVM, the accuracy of satellite image changedetection had been obviously improved, and overestimation of changed areas had been effectively reduced.
changedetection in Remote sensing image is, in essence, to detect the changes of ground features with regard to time from remote sensing perspective. It is usually realized by analyzing and processing multi-temporal ...
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changedetection in Remote sensing image is, in essence, to detect the changes of ground features with regard to time from remote sensing perspective. It is usually realized by analyzing and processing multi-temporal high resolution images. changedetection based on fully connected conditional random field not only improves the detection accuracy of remote sensing image, but also achieves better robustness. However, with the growth of high-resolution data volumes, this algorithm consumes a huge amount of time and computational resources, and therefore needs to be improved accordingly. Spark is an open-source distributed general- purpose cluster-computing framework. It has powerful memory computing and efficient task scheduling capabilities for complex iterative calculations. Based on Spark, this paper proposes a distributed and parallel method of changedetection in remote sensing image based on Fully Connected Conditional Random Field that analyzes the data input form, and proposes a multi-temporal image reading strategy on cloud platforms. This method decomposes the algorithm flow, and performs distributed parallel processing on each stage and makes full use of the processing advantages of data locality to implement a reasonable intermediate data storage. Experimental results demonstrate that this parallel method achieves a promising speedup with high scalability, while guaranteeing remarkable detection accuracy.
In this paper, we propose a novel changedetection method for temporal networks. In usual change detection algorithms, change scores are generated from an observed time series. When this change score reaches a thresho...
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ISBN:
(纸本)9781509052073
In this paper, we propose a novel changedetection 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 changedetection methods. Our analysis reveals how and when network centrality works well in terms of changedetection. 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 changedetection in temporal networks by utilizing network centralities.
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