Image changedetection has a wide range of applications in various fields, such as damage assessment, environmental monitoring and agricultural surveys. As the number of remote sensing images and the complexity of alg...
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ISBN:
(纸本)9781509033331
Image changedetection has a wide range of applications in various fields, such as damage assessment, environmental monitoring and agricultural surveys. As the number of remote sensing images and the complexity of algorithm rise, the demand for processing power is increasing. In this paper, we present a parallel FLICM algorithm for SAR image changedetection on Intel MIC (Many Integrated Core) which is the new type of many-core coprocessor. The proposed algorithm is implemented based on MIC-Offload mode using OpenMP (Open Multi-Processing). The parallel characteristics and implementation details of the proposed parallel FLICM algorithm are presented. Experiment results demonstrate that the optimized parallel algorithm can greatly reduce the computational time of the changedetection algorithm. The speedup is up to 20× compared with the runtime of serial algorithm on CPU.
In this paper, we propose a novel high resolution remote sensing image changedetection algorithm based on image fusion and fuzzy clustering models. The original images are fused based on the Wavelet to get the compre...
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ISBN:
(纸本)9781509012862
In this paper, we propose a novel high resolution remote sensing image changedetection algorithm based on image fusion and fuzzy clustering models. The original images are fused based on the Wavelet to get the comprehensive differencing map which reserves enough variation characteristics as well as reducing the noise. The idea of modifying traditional clustering algorithms will enhance the final result of the change map. This method is able to achieve the higher detection accuracy based on the revised fuzzy clustering algorithm and could obtain better differencing map with integration of image fusion. Experiments on real images point out the effectiveness and feasibility of the proposed methodology compared with other state-of-the-art ones visually and numerically.
Scene detection is pretreatment to index video information in video search system, and it is very important technology for overall performance. Existing scene detection used single characteristic of pixel value differ...
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Scene detection is pretreatment to index video information in video search system, and it is very important technology for overall performance. Existing scene detection used single characteristic of pixel value difference, histogram difference, etc or mixed single characteristics. However, accuracy of those is poor for specific video such as infrared camera, night shooting. This paper is proposed the method that is mixed color histogram and KLT algorithm for scene detection at the specific movie. We did an experiment which used color histogram only and KLT algorithm with color histogram. In result, proposed method is improved about 11.4% at the specific movie.
This work investigates the potential of an unsupervised network classifier, the Centroid Neural Network (CNN), for land cover changedetection in remotely sensed images. Experiments carried out to evaluate the algorit...
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This work investigates the potential of an unsupervised network classifier, the Centroid Neural Network (CNN), for land cover changedetection in remotely sensed images. Experiments carried out to evaluate the algorithm include changedetection in both approaches: pre-classification and post-classification. Results confirm the effectiveness of this technique.
Landslides often occur in mountains, where the low coherence characteristics result in decreased accuracy of traditional SAR-based change detection algorithms. In this paper, a non-local means filtering based coherenc...
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ISBN:
(数字)9798331515669
ISBN:
(纸本)9798331515676
Landslides often occur in mountains, where the low coherence characteristics result in decreased accuracy of traditional SAR-based change detection algorithms. In this paper, a non-local means filtering based coherence changedetection algorithm is proposed for multitemporal SAR images. First, Persistent Scatterers (PSs) and Distributed Scatterers (DSs) are both detected to improve the monitoring density in low coherence area. Then, coherence changedetection is adopted based on the multitemporal SAR images to replace traditional amplitude based changedetection algorithm. Finally, non-local means filtering is used to improve the changedetection accuracy of the whole area. The raw data of landslides occurred in Hong Kong is used to indicate the effectiveness of the proposed algorithm.
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.
Addresses the problem of the detection of speaker changes and clustering speakers when no information is available regarding speaker classes or even the total number of classes. We assume that no previous information ...
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Addresses the problem of the detection of speaker changes and clustering speakers when no information is available regarding speaker classes or even the total number of classes. We assume that no previous information on speakers is available (no speaker model, no training phase) and that people do not speak simultaneously. The aim is to apply speaker grouping information to speaker adaptation for speech recognition. We use vector quantization (VQ) distortion as the criterion. A speaker model is created from successive utterances as a codebook by a VQ algorithm, and the VQ distortion is calculated between the model and an utterance. A result was obtained by the experiment on speaker detection and speaker clustering. The speaker changedetection experiment was compared with results by generalized likelihood ratio and Bayesian information criterion. We show the superiority of our proposed method.
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.
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 ...
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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 changedetection. In an application of landslide monitoring, we demonstrated the use of subpixel changedetection 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 changedetection method can provide more detailed information about landslide spread than pixel-based change detection algorithms.
An automatic changedetection method based on conditional random field (CRF) is presented for high resolution remote sensing images in this paper. Marginalized denoising autoencoder is used to generate the difference ...
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An automatic changedetection method based on conditional random field (CRF) is presented for high resolution remote sensing images in this paper. Marginalized denoising autoencoder is used to generate the difference image. The clustering results of Fuzzy C-means are applied to initialize the unary potentials of CRF. A scaled squared Euclidean distance between neighboring pixels in the observed images is introduced to define the pairwise potentials of CRF, which avoid training parameters and help improve the accuracy and the degree of automation. The experimental results obtained on different remote sensing images demonstrated the accuracy and efficiency of our proposed method.
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