MRF model-based joint interrupted SAR imaging and coherent change detection via variational Bayesian inference

Yang, Yuexiang; Cong, Xunchao; Long, Kun; Luo, Yong; Xie, Wei; Wan, Qun

doi:10.1016/j.sigpro.2018.05.007

Cited by 26 publications

(8 citation statements)

References 31 publications

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“…Here, we also assume that ψ j on θ 1 and L are independent, and φ i on θ 2 and L are independent. Further, we use Markov random field assumption to model pðz | L, θÞ and treat the task assignment graph as a factor graph, of which variable nodes are task nodes, and factor nodes are worker nodes [30,31]. We use m i→j to denote the messages passing from tasks to workers and m j→i to denote the messages passing from workers to tasks.…”

Section: Preliminariesmentioning

confidence: 99%

A Cyber Physical System Crowdsourcing Inference Method Based on Tempering: An Advancement in Artificial Intelligence Algorithms

Liu

Tang

et al. 2021

Wireless Communications and Mobile Computing

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Activity selection is critical for the smart environment and Cyber-Physical Systems (CPSs) that can provide timely and intelligent services, especially as the number of connected devices is increasing at an unprecedented speed. As it is important to collect labels by various agents in the CPSs, crowdsourcing inference algorithms are designed to help acquire accurate labels that involve high-level knowledge. However, there are some limitations in the algorithm in the existing literature such as incurring extra budget for the existing algorithms, inability to scale appropriately, requiring the knowledge of prior distribution, difficulties to implement these algorithms, or generating local optima. In this paper, we provide a crowdsourcing inference method with variational tempering that obtains ground truth as well as considers both the reliability of workers and the difficulty level of the tasks and ensure a local optimum. The numerical experiments of the real-world data indicate that our novel variational tempering inference algorithm performs better than the existing advancing algorithms. Therefore, this paper provides a new efficient algorithm in CPSs and machine learning, and thus, it makes a new contribution to the literature.

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Section: Preliminariesmentioning

confidence: 99%

A Cyber Physical System Crowdsourcing Inference Method Based on Tempering: An Advancement in Artificial Intelligence Algorithms

Liu

Tang

et al. 2021

Wireless Communications and Mobile Computing

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“…Currently, a lot of change detection methods have been reported to detect the changed information on this earth we live. These change detection methods can be roughly grouped into three categories: pixel-based approaches [8][9][10][11][12][13][14][15][16][17][18][19], objectbased approaches [20][21][22][23][24][25], and deep learning (DL) based approaches [26][27][28][29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

“…Compared to pixel-based approaches, the object-based approaches provide higher accuracy and stronger robustness than pixel-based methods, because they can effectively integrate richer features of objects for comprehensive evaluation and analysis, such as shape, texture and spectral information of images. The object-based approaches mainly include Markov Random Fields (MRF) based methods [20][21][22][23] and level set based methods [24,25]. These methods are popular for change detection tasks because they can consider the spatial relationship of neighborhood pixels.…”

Section: Introductionmentioning

confidence: 99%

“…Touati et al [22] filled the gap by proposing Bayesian and Markovianbased multimodal method. Also, Yang et al [23] devoted change detection tasks using special MRF prior and variational Bayesian inference, and [23] achieved a good performance in respect of shape preserving ability. For level set based change detection methods, Li et al [24] employed edge-based level set evolution (ELSE) and region-based level set evolution (RLSE) to track initial change detection profiles, leading to better change detection results.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised Change Detection Using Multiscale and Multiresolution Gaussian-Mixture-Model Guided by Saliency Enhancement

Xue

Lei

Jia

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In general, change detection algorithm consists of three steps: 1) image preprocessing; 2) difference image (DI) formation of a pair of multi-temporal images; 3) DI image analysis to achieve segmentation of the changed regions [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

SAR Image Change Detection Based on Data Optimization and Self-Supervised Learning

Wang

et al. 2020

IEEE Access

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In the SAR change detection algorithm based on self-supervised learning, speckle noise reduces the difference image (DI) quality. Therefore, the contrast of the DI is low, and its change area is not significant. Moreover, the preclassification algorithm with the poor robustness makes the classification results of the low-quality DI inaccurate. When the wrong labels are sent into the classification network, the accuracy of the final detection results is reduced. First, to improve the quality of the initial DI, we design an adaptive gamma correction algorithm that adjusts the contrast according to the mean value of the initial DI and the variation coefficient β. The contrast of the new DI generated by this algorithm is higher. Furthermore, to suppress the noise, we adopt a new algorithm based on popular ranking to obtain the saliency map of the new DI. Combining the initial DI with this saliency map, a high-quality DI with a low noise level is obtained. After that, we introduce the structure tensor into the fuzzy local information c-means clustering algorithm (FLICM) to classify the DI more accurately. The new clustering algorithm improves the accuracy of preclassification, especially its hierarchical version. Besides, we use the structure tensor to generate the structure maps of the original images. Finally, according to the prior information obtained from the preclassification, we use a convolution wavelet neural network (CWNN) to supervise and train the structure maps of the original images. Experimental results show that the DI generated by us is closer to the groundtruth than other methods. Our preclassification algorithm performs better. Our algorithm shows higher detection accuracy for SAR images with strong noise than some advanced change detection algorithms. INDEX TERMS Adaptive gamma correction, convolution wavelet neural network, fuzzy local information c-means clustering, saliency map, structure tensor.

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MRF model-based joint interrupted SAR imaging and coherent change detection via variational Bayesian inference

Cited by 26 publications

References 31 publications

A Cyber Physical System Crowdsourcing Inference Method Based on Tempering: An Advancement in Artificial Intelligence Algorithms

A Cyber Physical System Crowdsourcing Inference Method Based on Tempering: An Advancement in Artificial Intelligence Algorithms

Unsupervised Change Detection Using Multiscale and Multiresolution Gaussian-Mixture-Model Guided by Saliency Enhancement

SAR Image Change Detection Based on Data Optimization and Self-Supervised Learning

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