In real life changedetection for remotely sensed images suffers due to the problem of inadequate labeled patterns. When a few labeled patterns can be collected by experts, semi-supervised (learning) clustering can be...
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In real life changedetection for remotely sensed images suffers due to the problem of inadequate labeled patterns. When a few labeled patterns can be collected by experts, semi-supervised (learning) clustering can be opted for changedetection instead of the unsupervised approach to make full utilization of both labeled and unlabeled patterns. In the present work, a study has been carried out by applying some of the semi-supervised clustering techniques for changed detection. A comparative analysis between K-Means, COP-KMeans, Seeded-KMeans and Constrained-KMeans algorithms is being performed based on the results obtained using two multi-temporal remotely sensed images. It can be concluded from the experiments that the Constrained-KMeans is well suited for changed detection of remotely sensed images under semi-supervised framework.
Here we propose a methodology to combine the output of fuzzy clusterings to detect changes in remote sensing images. In this regard we select two fuzzy clustering algorithms, namely fuzzy c-means (FCM) and Gustafson K...
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Here we propose a methodology to combine the output of fuzzy clusterings to detect changes in remote sensing images. In this regard we select two fuzzy clustering algorithms, namely fuzzy c-means (FCM) and Gustafson Kessel clustering (GKC). For clustering purpose various image features are extracted using the neighborhood information of pixels from the difference image (DI). To assign a pixel-pattern to either of the two groups (for changed and unchanged regions of the DI) maximum of the two membership-values (given by FCM and by GKC for the same pattern for the same cluster) is considered. It has been observed experimentally that the changesare detected more efficiently using the proposed ensemble-based procedure. To show the effectiveness of the proposed technique, experiments are conducted on two multispectral and multitemporal remote sensing images. Results are compared with those of existing stand-alone fuzzy clustering based techniques, Markov random field (MRF) & neural network based algorithms and found to be superior.
To take into account slow changes of characteristics in signal which are only locally stationary, an adaptive process for parameter modeling is carried out. An adaptive parametric modeling technique using windows is d...
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To take into account slow changes of characteristics in signal which are only locally stationary, an adaptive process for parameter modeling is carried out. An adaptive parametric modeling technique using windows is described. A general process with two thresholds for changedetection is discussed. A more general method based on the histograms of the prediction errors is suggested.< >
Several studies have reported the potentialities of high resolution multi-spectral imagery for classifying and monitoring urban areas [A. K. Shackelford et al. (2003)], [M. Pesaresi et al. (2000)], [G. Schiavon et al....
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Several studies have reported the potentialities of high resolution multi-spectral imagery for classifying and monitoring urban areas [A. K. Shackelford et al. (2003)], [M. Pesaresi et al. (2000)], [G. Schiavon et al. (2003)]. In this paper we present the results obtained by processing high resolution multispectral QuickBird images of an urban area. The high resolution QuickBird data have been used for two different purposes: for an automatic image classification using neural network techniques and for a changedetection analysis. In the first case, we have carried out a pixel-based classification procedure aimed at the discrimination among 4 main classes: buildings, roads, vegetated areas, bare soil; then we have examined the potentialities of Kohonen maps for discovering new subclasses within those already established: e.g. for the asphalt category, different subclasses such as highways pixels and the other different types of roads such as secondary street pixels have been identified. In the second case we have processed multitemporal QuickBird images for detecting major changes occurred over the selected test area, like news buildings not visible in the first image.
The inherent speckle noise in synthetic aperture radar (SAR) images limits the accuracy of SAR image changedetection. As a crucial step in unsupervised changedetection, existing difference map generation methods pri...
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The inherent speckle noise in synthetic aperture radar (SAR) images limits the accuracy of SAR image changedetection. As a crucial step in unsupervised changedetection, existing difference map generation methods primarily utilize neighborhood information to counteract the interference caused by speckle noise. However, pixels within the neighborhood can themselves be affected by heterogeneous pixels and noise. Therefore, this letter proposes a difference map generation method, partial neighborhood ratio (PNR), which relies on high-confidence homogeneous pixels within the neighborhood for difference calculation. Specifically, under the assumption that the local neighborhood of SAR images follows a normal distribution, we develop a method for selecting high-confidence homogeneous pixels. This method quantifies interneighborhood dissimilarity by leveraging the statistical features of predominantly homogeneous pixel clusters within an adaptive framework, thereby reducing the impact of noise and enhancing the accuracy of difference expression. Experimental results demonstrate the superior performance of the proposed PNR. The changedetection results, obtained by applying both manual trial-and-error and dual-domain network (DDNet) on three SAR datasets, have validated the effectiveness of the proposed algorithm.
This letter proposes a changedetection algorithm for damage assessment caused by fires in Ireland using Sentinel 1 data. The novelty, in this letter, is a feature extraction within tunable Q discrete wavelet transfor...
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This letter proposes a changedetection algorithm for damage assessment caused by fires in Ireland using Sentinel 1 data. The novelty, in this letter, is a feature extraction within tunable Q discrete wavelet transform (TQWT) using higher order log cumulants of fractional Fourier transform (FrFT), which were fed into a stacked autoencoder (SAE) to distinguish changed and unchanged areas. The extracted features were used to train the SAE layerwise using an unsupervised learning algorithm. After training the decoding layer was replaced by a logistic regression layer to perform supervised fine-tuning and classification. The proposed algorithm was compared with the algorithm that used log cumulants of FrFT within the oriented dual-tree wavelet transform using support vector machine (SVM) classifier. The experimental results showed that the proposed combination of algorithms decreased the overall error (OE) for real synthetic aperture radar images by 6%, when TQWT was used instead of oriented dual-tree wavelet transform and OE was decreased by another 5% when SAE was used instead of the SVM classifier.
We consider the problem of person-in-bed detection using accelerometer measurements in the segmented as well as streaming setting. For the segmented problem, we identify frequency domain features (4 features for each ...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
We consider the problem of person-in-bed detection using accelerometer measurements in the segmented as well as streaming setting. For the segmented problem, we identify frequency domain features (4 features for each acceleration coordinate) that can be used to model the in-bed and not-in-bed hypotheses. We estimate the model parameters from the training data and apply the Generalized Likelihood Ratio (GLR) test. Using the same form as the GLR test statistic, we also propose an improvement using quadratic logistic regression. For the streaming problem, we model it as a sequential changedetection problem using the models that we obtained for the in-bed and not-in-bed hypotheses and propose a GLRT-based Cumulative Sum (CuSum) algorithm.
A new class of stochastic processes called independent and periodically identically distributed (i.p.i.d.) processes is defined to capture periodically varying statistical behavior. A novel Bayesian theory is develope...
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A new class of stochastic processes called independent and periodically identically distributed (i.p.i.d.) processes is defined to capture periodically varying statistical behavior. A novel Bayesian theory is developed for detecting a change in the distribution of an i.p.i.d. process. It is shown that the Bayesian change point problem can be expressed as an optimal control problem of a Markov decision process (MDP) with periodic transition and cost structures. An optimal control theory is developed for periodic MDPs for discounted and undiscounted total cost criteria. A fixed-point equation is obtained that is satisfied by the optimal cost function. It is shown that a nonstationary but periodic policy is optimal. A value iteration algorithm is obtained to compute the optimal cost function. The results from the MDP theory are then applied to detect a change in the distribution of an i.p.i.d. process. It is shown that while a stopping rule based on a periodic sequence of thresholds is exactly optimal, a single-threshold policy is asymptotically optimal, as the probability of false alarm goes to zero. Numerical results are provided to demonstrate that the asymptotically optimal policy is not strictly optimal.
changedetection is the foundation of intelligent video surveillance of the airport ground. However, experiments have shown that change detection algorithms with good performance on traditional datasets (e.g., CDnet20...
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changedetection is the foundation of intelligent video surveillance of the airport ground. However, experiments have shown that change detection algorithms with good performance on traditional datasets (e.g., CDnet2014) perform poorly in airport ground surveillance. The reason is that traditional datasets focus on the diversity of scenarios, while the practical application requires robustness against various changes in a single scene. We posit that the solution to this problem is to establish a unique dataset for airport ground surveillance and develop specific algorithms for this scenario. In this paper, we present an Airport Ground Video Surveillance benchmark (AGVS) for changedetection of the airport ground. AGVS includes 25 long videos, amounting to about 100000 frames and accurate ground truth for all frames. Each video contains multiple challenges specific to the airport ground (e.g., haze, camouflage, strip shape, shadow and illumination change, simultaneous multi-scale objects) and various appearance changes of the aircraft). changedetection ground truth is generated by manual annotation. The AGVS benchmark can be downloaded from ***. Furthermore, we conduct a simple review of current change detection algorithms, both unsupervised or supervised, and then 21 state-of-the-art algorithms are tested and analyzed on the AGVS benchmark. Finally, we conclude with algorithm design principles of changedetection for airport ground surveillance.
We describe a new method for the automatic detection of changes in repeat CT scanning with a reduced X-ray radiation dose. We present a theoretical formulation of the automatic changedetection problem based on the on...
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We describe a new method for the automatic detection of changes in repeat CT scanning with a reduced X-ray radiation dose. We present a theoretical formulation of the automatic changedetection problem based on the on-line sparse-view repeat CT scanning dose optimization framework. We prove that the changedetection problem is NP-hard and therefore cannot be efficiently solved exactly. We describe a new greedy changedetection algorithm that is simple and robust and relies on only two key parameters. We demonstrate that the greedy algorithm accurately detects small, low contrast changes with only 12 scan angles. Our experimental results show that the new algorithm yields a mean changed region recall rate > 89% and a mean precision rate > 76%. It outperforms both our previous heuristic approach and a thresholding method using a low-dose prior image-constrained compressed sensing (PICCS) reconstruction of the repeat scan. The resulting changed region map may obviate the need for a high-quality repeat scan image when no major changes are detected and may streamline the radiologist's workflow by highlighting the regions of interest.
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