Unsupervised change detection in SAR images using curvelet and L1-norm based soft segmentation

Li, Fang; Fang, Faming; Zhang, Guixu

doi:10.1080/01431161.2016.1196838

Cited by 11 publications

(4 citation statements)

References 32 publications

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“…Formula (1) describes that the data G(z) generated by G in the process of continuous antagonistic learning is getting closer to the real sample, and the discrimination of G(z) by D is becoming more and more blurred. The loss function of D is calculated by Formula (2):…”

Section: Gan Network Modelmentioning

confidence: 99%

“…The system consists of a generator and a discriminator. The generator captures the potential distribution of real data samples and generates new data samples; the discriminator is a binary classifier used to determine whether the input is real data or generated samples [1,2]. Both the generator and the discriminator can use the currently heated deep neural network.…”

Section: Introductionmentioning

confidence: 99%

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Edge Detection Algorithm of a Symmetric Difference Kernel SAR Image Based on the GAN Network Model

Zhang

Liu

et al. 2019

Symmetry

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The symmetrical difference kernel SAR image edge detection algorithm based on the Canny operator can usually achieve effective edge detection of a single view image. When detecting a multi-view SAR image edge, it has the disadvantage of a low detection accuracy. An edge detection algorithm for a symmetric difference nuclear SAR image based on the GAN network model is proposed. Multi-view data of a symmetric difference nuclear SAR image are generated by the GAN network model. According to the results of multi-view data generation, an edge detection model for an arbitrary direction symmetric difference nuclear SAR image is constructed. A non-edge is eliminated by edge post-processing. The Hough transform is used to calculate the edge direction to realize the accurate detection of the edge of the SAR image. The experimental results show that the average classification accuracy of the proposed algorithm is 93.8%, 96.85% of the detection edges coincide with the correct edges, and 97.08% of the detection edges fall into the buffer of three pixel widths, whichshows that the proposed algorithm has a high accuracy of edge detection for kernel SAR images.

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Section: Gan Network Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Edge Detection Algorithm of a Symmetric Difference Kernel SAR Image Based on the GAN Network Model

Zhang

Liu

et al. 2019

Symmetry

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show abstract

“…Image change detection aims to detect the changed areas in images of the same scene taken at different points in time [1,2]. Over the last three decades, many different methods have been reported for detecting a changing area [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Detection of Road Surface Changes from Multi-Temporal Unmanned Aerial Vehicle Images Using a Convolutional Siamese Network

Nguyen

Han

2020

Sustainability

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Road quality commonly decreases due to aging and deterioration of road surfaces. As the number of roads that need to be surveyed increases, general maintenance—particularly surveillance—can be quite costly if carried out using traditional methods. Therefore, using unmanned aerial vehicles (UAVs) and deep learning to detect changes via surveys is a promising strategy. This study proposes a method for detecting changes on road surfaces using pairs of UAV images captured at different times. First, a convolutional Siamese network is introduced to extract the features of an image pair and a Euclidean distance function is applied to calculate the distance between two features. Then, a contrastive loss function is used to enlarge the distance between changed feature pairs and reduce the distance between unchanged feature pairs. Finally, the initial change map is improved based on the preliminary differences between the two input images. Our experimental results confirm the effectiveness of this approach.

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“…Furthermore, the differences among dynamometer cards are mainly reflected in the scales and the directions. Curvelet Transform is a multi-resolution method which has been widely used to handle feature extraction problems 8,9,10 . It is not only a multi-scale but also a multi-direction transform, so features extracted by Curvelet Transform are very sensitive to the shapes of dynamometer cards.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of sucker rod pumping systems based on Curvelet Transform and sparse multi-graph regularized extreme learning machine

Zhang¹,

Gao²

2018

IJCIS

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A novel approach is proposed to complete the fault diagnosis of pumping systems automatically. Fast Discrete Curvelet Transform is firstly adopted to extract features of dynamometer cards that sampled from sucker rod pumping systems, then a sparse multi-graph regularized extreme learning machine algorithm (SMELM) is proposed and applied as a classifier. SMELM constructs two graphs to explore the inherent structure of the dynamometer cards: the intra-class graph expresses the relationship among data from the same class and the inter-class graph expresses the relationship among data from different classes. By incorporating the information of the two graphs into the objective function of extreme learning machine (ELM), SMELM can force the outputs of data from the same class to be as same as possible and simultaneously force results from different classes to be as separate as possible. Different from previous ELM models utilizing the structure of data, our graphs are constructed through sparse representation instead of K-nearest Neighbor algorithm. Hence, there is no parameter to be decided when constructing graphs and the graphs can reflect the relationship among data more exactly. Experiments are conducted on dynamometer cards acquired on the spot. Results demonstrate the efficacy of the proposed approach for faults diagnosis in sucker rod pumping systems.

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Unsupervised change detection in SAR images using curvelet and L1-norm based soft segmentation

Cited by 11 publications

References 32 publications

Edge Detection Algorithm of a Symmetric Difference Kernel SAR Image Based on the GAN Network Model

Edge Detection Algorithm of a Symmetric Difference Kernel SAR Image Based on the GAN Network Model

Detection of Road Surface Changes from Multi-Temporal Unmanned Aerial Vehicle Images Using a Convolutional Siamese Network

Fault diagnosis of sucker rod pumping systems based on Curvelet Transform and sparse multi-graph regularized extreme learning machine

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