This study proposes a new three-component method for timely detection of land cover changes using polarimetric synthetic aperture radar (PolSAR) images. The three components are object-oriented image analysis (OOIA), ...
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This study proposes a new three-component method for timely detection of land cover changes using polarimetric synthetic aperture radar (PolSAR) images. The three components are object-oriented image analysis (OOIA), change vector analysis (CVA), and post-classification comparison (PCC). First, two PolSAR images acquired over the same area at different dates are segmented hierarchically to delineate land parcels (image objects). Then, parcel-based CVA is performed with the coherency matrices of the PoISAR data to detect changed parcels. Finally, PCC based on a parcel-based classification algorithm integrating polarimetric decomposition, decision tree algorithms, and support vector machines is used to determine the type of change for the changed parcels. Compared with conventional PCC based on the widely used Wishart supervised classification, the three-component method achieves much higher accuracy for land cover changedetection with PoISAR images. The contribution of each component is evaluated by excluding it from the method. The integration of OOIA in the method greatly reduces the false alarms caused by speckle noise in PoISAR images as well as improves the accuracy of PoISAR image classification. CVA contributes to the method by significantly reducing the effect of the classification errors on the changedetection. The use of PCC in the method not only identifies different types of land cover change but also reduces the false alarms introduced by the change in the environment. The three-component method is validated in land development detection, which is important to many developing countries that are confronting a growing problem of unauthorized construction land expansion. The results show that the three-component method is effective in detecting land developments with PoISAR images. (C) 2015 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
changedetection is the process of automatically identifying and analyzing region that have undergone spatial or spectral changes from multi temporal images. Detecting and representing change provides valuable informa...
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ISBN:
(纸本)9781467385497
changedetection is the process of automatically identifying and analyzing region that have undergone spatial or spectral changes from multi temporal images. Detecting and representing change provides valuable information of the possible transformations a given scene has suffered over time. changedetection is used in several applications (eg. Disaster management, deforestation, urbanization, etc). In the proposed unsupervised method co-registered and radiometrically corrected temporal images are used as input. Using this, absolute valued image and log ratio image is calculated to get difference image. These difference images are fused using Discrete Wavelet Transform (DWT). Then, min-mean normalization is applied to the get filtered data. The normalized data is clustered into two groups using K-means clustering algorithm as changed pixels and unchanged pixels. Experiment result is also calculated using two different ways. In first, fused image data is given to Principal Component Analysis (PCA) and clustering is done using K-means algorithm and in second way Fuzzy c-means clustering algorithm is used to cluster image data.
In this paper we propose a novel methodology of sequential changedetection using the minimum description length (MDL)-change statistics. We first introduce the MDL-change statistics as the difference between the code...
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ISBN:
(纸本)9781467390064
In this paper we propose a novel methodology of sequential changedetection using the minimum description length (MDL)-change statistics. We first introduce the MDL-change statistics as the difference between the code-lengths with change and that without change. We give a theoretical justification for its use in the scenario of hypothesis testing. In it we evaluate the error probabilities for the MDL-changedetection to relate them to the information-theoretic complexities of the probabilistic models and their discrepancy measure. We then convert the MDL-change statistics into the sequential changedetection algorithm. It is designed to detect gradual changes as well as abrupt changes from big stream data. We empirically demonstrate the effectiveness of the proposed method by showing that it performs better than existing algorithms for synthetic data. We also show its validity through real problems such as SQL injection detection and failure symptom detection.
We propose a statistical algorithm for detecting line outages in a power system and show that it has better performance than other schemes proposed in the literature. Our algorithm is based on the Cumulative Sum (CuSu...
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ISBN:
(纸本)9781479999897
We propose a statistical algorithm for detecting line outages in a power system and show that it has better performance than other schemes proposed in the literature. Our algorithm is based on the Cumulative Sum (CuSum) test from the Quickest changedetection (QCD) literature. It exploits the statistical properties of the measured voltage phase angles before, during, and after a line outage, whereas other methods in the literature only utilize the change in statistics that occurs at the instant of outage.
In this article, Fast Global K-Means (FGKM) for Synthetic Aperture Radar (SAR) image changedetection is presented. On account of the time-consuming of FGKM algorithm and the real-time demand, we present a Parallel Fa...
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ISBN:
(纸本)9781479979301
In this article, Fast Global K-Means (FGKM) for Synthetic Aperture Radar (SAR) image changedetection is presented. On account of the time-consuming of FGKM algorithm and the real-time demand, we present a Parallel Fast Global K-Means (P-FGKM) algorithm. We parallelize the selection of initial cluster centers which is the most time-consuming step of FGKM algorithm. The proposed algorithm is implemented based on Open Computing Language (OpenCL). The experiments are carried out on a variety of heterogeneous computing devices, such as Multi-core CPU, GPU, Intel HD Graphics, Many Integrated Core (MIC). Experiment results show that the proposed algorithm can achieve a good speedup up to 86 times on such devices.
In this paper, a novel unsupervised changedetection algorithm based on game theory is proposed for synthetic aperture radar(SAR) images. With the introduction of Nash-game theory, we find the balance of segmentation ...
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ISBN:
(纸本)9781479975358
In this paper, a novel unsupervised changedetection algorithm based on game theory is proposed for synthetic aperture radar(SAR) images. With the introduction of Nash-game theory, we find the balance of segmentation accuracy and overall restoration performance. Restoration of images plays a denoising role due to the complex movement while obtaining a SAR image. The Segmentation procedure transfers the difference map into change map. To make the algorithm less time-consuming, we analyze the state-of-the-art methods for generating the change map and finally select the minus map as the preferred one. The experiment based on the proposed methodology proves the accuracy and robustness of our algorithm compared with several well-known changedetection techniques on both noise-free and noisy satellite images. Further optimization methods are discussed in the end.
Post-Classification Comparison(PCC) method is widely used in changedetection for remote sensing images, but it is affected by a significant cumulative error caused by single remote sensing image classification during...
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ISBN:
(纸本)9781479979301
Post-Classification Comparison(PCC) method is widely used in changedetection for remote sensing images, but it is affected by a significant cumulative error caused by single remote sensing image classification during changedetection, which leads to the excessive evaluation of changed types and quantity. To solve this problem, this paper proposes a changedetection method for remote sensing images based on Adaptive Resonance Theory Mapping (ARTMAP) neural network. Similarity matrix is constructed by spectral feature vectors. Then the threshold value of similarity is obtained, which is used to control the joint-classification classifier based on the ARTMAP neural network. In addition, an adaptive algorithm of vigilance parameter is introduced to the classification process of fuzzy ARTMAP neural network. The experimental results obtained on remote sensing images show that the proposed method not only accurately classifies the unchanged geographical information in different temporal images into the same class, but also reduces the cumulative error and improves the accuracy of changedetection compared with other methods.
Several changedetection methods have been developed over the last decades and an even higher number during the last years due to the opening of the Landsat archive. Some changedetection methods aim at all types of l...
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ISBN:
(纸本)9781479979301
Several changedetection methods have been developed over the last decades and an even higher number during the last years due to the opening of the Landsat archive. Some changedetection methods aim at all types of land surface alterations while others target specific types such as inundations, urbanization or forest cover change or even more specifically forest cover loss. Many methods were developed and tested for temperate regions where most cloud-free data are obtained during the growing season. In the tropics, however, cloud cover is highest during the period when vegetation is most active. This study tests two common approaches, the Vegetation change Tracker (VCT) and the Iterative Multivariate Alteration detection (IMAD) in the northwestern portion of the Yucatan peninsula using Landsat images. Results from changedetection algorithm were compared to reference samples and reference polygons. Various parameter sets for the VCT algorithm never reach the accuracy level of IMAD.
This paper addresses the problem of changedetection from very high resolution remotely sensed images and its application on road damage extraction in case of major disaster. The proposed methodology is based on the m...
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This paper addresses the problem of changedetection from very high resolution remotely sensed images and its application on road damage extraction in case of major disaster. The proposed methodology is based on the multiscale image segmentation using the Haar wavelet in order to define the appropriate unit of analysis for the comparison step. The Kullback-Leibler divergence is then applied as a similarity measurement to identify changed regions. This strategy is adapted to solve the road damage extraction problem by applying the Dempster-Shafer theory (DST). The images acquired during the earthquake that hits Port-au-Prince (Haiti) on 12 January 2010 are used in the experimentations and the obtained results demonstrate the accuracy and the efficiency of the described method.
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