When changes are performed on an automated production system (aPS), new faults can be accidentally introduced into the system, which are called regressions. A common method for finding these faults is regression testi...
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When changes are performed on an automated production system (aPS), new faults can be accidentally introduced into the system, which are called regressions. A common method for finding these faults is regression testing. In most cases, this regression testing process is performed under high time pressure and onsite in a very uncomfortable environment. Until now, there has been no automated support for finding and prioritizing system test cases regarding the fully integrated aPS that are suitable for finding regressions. Thus, the testing technician has to rely on personal intuition and experience, possibly choosing an inappropriate order of test cases, finding regressions at a very late stage of the test run. Using a suitable prioritization, this iterative process of finding and fixing regressions can be streamlined and a lot of time can be saved by executing test cases likely to identify new regressions earlier. Thus, an approach is presented in this paper that uses previously acquired runtime data from past test executions and performs a change identification and impact analysis to prioritize test cases that have a high probability to unveil regressions caused by side effects of a system change. The approach was developed in cooperation with reputable industrial partners active in the field of aPS engineering, ensuring a development in line with industrial requirements. An industrial case study and an expert evaluation were performed, showing promising results.
Noise interference and the need to process massive image data present challenges to changedetection in synthetic aperture radar (SAR) images. In order to improve the changedetection accuracy and decrease the process...
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Noise interference and the need to process massive image data present challenges to changedetection in synthetic aperture radar (SAR) images. In order to improve the changedetection accuracy and decrease the processing time, this paper proposes a novel unsupervised changedetection algorithm for SAR images. The logarithmic transformation is applied to transform images into the logarithmic domain, while the multiplicative noise in images is transformed into additive noise. The total variation (TV) denoising algorithm is then used to reduce image noise, and the difference operator in the logarithmic domain is employed to provide the difference image. The k-means clustering algorithm, which does not require consideration of the statistical properties of an image, is employed to cluster the difference image into two disjointed classes: changed and unchanged. The experimental results demonstrate that changedetection results achieved by the proposed algorithm offer great improvement over existing algorithms in terms of objective quantitative indices and the visual effect.
Estimation of the surface elevation change of the Greenland Ice Sheet (GrIS) is essential for understanding its response to recent and future climate change. Laser measurements from the NASA's Ice, Cloud, and land...
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Estimation of the surface elevation change of the Greenland Ice Sheet (GrIS) is essential for understanding its response to recent and future climate change. Laser measurements from the NASA's Ice, Cloud, and land Elevation Satellite (ICESat) created altimetric surveys of GrIS surface elevations over the 2003-2009 operational period of the mission. This paper compares four changedetection methods using Release 634 ICESat laser altimetry data: repeat tracks (RTs), crossovers (XOs), overlapping footprints (OFPs), and triangulated irregular networks (TINs). All four methods begin with a consistently edited data set and yield estimates of volumetric loss of ice from the GrIS ranging from -193 to -269 km(3)/yr. Using a uniform approach for quantifying uncertainties, we find that volume change rates at the drainage system scale from the four methods can be reconciled within 1-sigma uncertainties in just 5 of 19 drainage systems. Ice-sheet-wide volume change estimates from the four methods cannot be reconciled within 1-sigma uncertainties. Our volume change estimates lie within the range of previously published estimates, highlighting that the choice of method plays a dominant role in the scatter of volume change estimates. We find that for much of the GrIS, the OFP and TIN methods yield the lowest volume change uncertainties because of their superior spatial distribution of elevation change rate estimates. However, the RT and XO methods offer inherent advantages, and the future work to combine the elevation changedetection methods to produce better estimates is warranted.
This paper develops distortion metrics for compressed synthetic aperture radar (SAR) imagery from changedetection test statistics. These metrics are used to predict lossy image compression's impact on change dete...
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This paper develops distortion metrics for compressed synthetic aperture radar (SAR) imagery from changedetection test statistics. These metrics are used to predict lossy image compression's impact on changedetection performance. The metrics do not require the intended changedetection comparison image to provide these benefits. An SAR compression system leveraging the distortion metrics is proposed. The system generates a bad-pixel mask highlighting potential false alarms that are generated due to compression and are subsequently discarded in the changedetection process. The proposed system's performance is demonstrated through noncoherent changedetection analysis after JPEG2000 and JPEG image compression. Similarly, a coherent changedetection system is evaluated after JPEG2000 image compression. For noncoherent changedetection at large compression ratios (CRs) using JPEG2000, the proposed system provides a 33% reduction in false alarms at a 0.1 probability of detection as well as the ability to maintain near-distortionless false alarm rates across a wide range of CRs. At a 0.1 probability of detection for coherent changedetection, the system provides a 37% reduction in false alarms at modest CRs. The coherent changedetection system is also demonstrated to maintain low false alarm rates across a range of CRs.
This paper presents a method for the detection of faults in electrical pumps. The method relies on the computation of two features, the pump efficiency and the hydraulic balance, that present reference values during h...
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The thirteen papers in this special section were presented at the 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp 2017), hosted by VITO Remote Sensing on June 27-29, 2017.
The thirteen papers in this special section were presented at the 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp 2017), hosted by VITO Remote Sensing on June 27-29, 2017.
In the past decades, land cover changedetection (LCCD) has been dramatically developed, since it provides corroborative support for policy decision, regulatory actions, and subsequent urban-rural activities. Satellit...
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In the past decades, land cover changedetection (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 changedetection 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.
With a continuous increase in multi-temporal synthetic aperture radar (SAR) images, leading to enable mapping applications for Earth environmental observation, the number of algorithms for detection of different types...
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With a continuous increase in multi-temporal synthetic aperture radar (SAR) images, leading to enable mapping applications for Earth environmental observation, the number of algorithms for detection of different types of terrain changes has greatly expanded. In this paper, a SAR image changedetection method based on normalized compression distance (NCD) is proposed. The procedure mainly consists in dividing two time series images in patches, computing a collection of similarities corresponding to each pair of patches and generating the change map with a histogram-based threshold. The experimental results were computed using 2 Sentinel 1A images over the city of Bucharest, Romania and 2 TerraSAR-X images over the Elbe River and its surrounding area, Germany.
We present a high performance hybrid method for unsupervised changedetection in multi-temporal satellite images using singular values and clustering, an important problem for satellite image processing. We partition ...
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ISBN:
(纸本)9781728113616
We present a high performance hybrid method for unsupervised changedetection in multi-temporal satellite images using singular values and clustering, an important problem for satellite image processing. We partition the difference image into small data blocks with non-overlapping and overlapping pixels and use these data blocks to justify the changing status of the contents. However, instead of building an eigen space from the orthonormal eigenvectors extracted through Principal Component Analysis (PCA) of the data blocks, we analyze the singular values and use only a small subset of the values. This eigen space with much smaller number of dimensions helped to significantly improve the efficiency of the changedetection algorithm. The changedetection is achieved by partitioning the feature vector space into two clusters using clustering algorithms. We utilize the designs of scalable and efficient parallel algorithms for many-core GPU devices using CUDA. Our algorithms expose substantial fine-grained parallelism while maintaining minimal global communication. Experimental results using real-world satellite images showed that our algorithm works efficiently with real world satellite images. We also tested our algorithm under different noise conditions and found that it is stable and accurate.
When the selection of the initial points in the Fast-Independent Component Analysis (Fast-ICA) algorithm is far from the minimum point, the problem of convergence may not be convergent, and an improved Fast-ICA algori...
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When the selection of the initial points in the Fast-Independent Component Analysis (Fast-ICA) algorithm is far from the minimum point, the problem of convergence may not be convergent, and an improved Fast-ICA algorithm based on the damped Newton method is proposed. The algorithm adds one dimension search along the direction of Newton iteration, thus ensuring the convergence of Fast-ICA algorithm. Secondly, the Contourlet transform is combined with the improved Fast-ICA. When using the combined method for changedetection, the initial transformation of remote sensing image is first used by Contourlet transform; secondly, the modified Fast-ICA algorithm is used to detect the change; then the image after the changedetection is invert with Contourlet; finally, the image is segmented by threshold value, and the change results are obtained. The experimental results show that, compared with the changedetection results based on the traditional Fast-ICA algorithm, the proposed method can effectively separate the information of the change, and the detection precision is higher.
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