Auroral oval segmentation using dual level set based on local information

Yang, Pinglv; Zhou, Zeming; Shi, Hanqing; Yan, Meng

doi:10.1080/2150704x.2017.1354260

Cited by 6 publications

(12 citation statements)

References 18 publications

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“…LIDLSM [26] is a level set method explicitly designed for auroral oval segmentation. In LIDLSM, Yang et al [26] utilized dual level set function of φ 1 and φ 2 to obtain the inner and outer auroral oval boundaries, respectively. Suppose that I : Ω ⊂ R 2 is an image domain, I : Ω → R 2 is the input auroral oval image.…”

Section: Spflif-ismentioning

confidence: 99%

“…This can be shown in Figure 3. Assuming that the distance between and follows the Gaussian distribution, Yang et al [26] introduced the shape energy term into LIDLSM. Besides, the local information term was constructed to improve the segmentation performance in the low contrast region, and the regularization term was adopted to ensure the curve can evolve smoothly towards the auroral oval Assuming that the distance between φ 1 and φ 2 follows the Gaussian distribution, Yang et al [26] introduced the shape energy term into LIDLSM.…”

Section: Spflif-ismentioning

confidence: 99%

“…Assuming that the distance between and follows the Gaussian distribution, Yang et al [26] introduced the shape energy term into LIDLSM. Besides, the local information term was constructed to improve the segmentation performance in the low contrast region, and the regularization term was adopted to ensure the curve can evolve smoothly towards the auroral oval Assuming that the distance between φ 1 and φ 2 follows the Gaussian distribution, Yang et al [26] introduced the shape energy term into LIDLSM. Besides, the local information term was constructed to improve the segmentation performance in the low contrast region, and the regularization term was adopted to ensure the curve can evolve smoothly towards the auroral oval boundaries and inhibit small isolated regions.…”

Section: Spflif-ismentioning

confidence: 99%

“…The 150 test images that do not participate in the training process are used for validating the performance of the DFALS method. The experimental results are also compared with other auroral oval segmentation models and recently developed level set methods, i.e., SIIALSM [25], LIDLSM [26], RLSF [23], and SPFLIF-IS [24]. The parameters of the DFALS are empirically set as follows: γ 1 = 1, γ 2 = 2, γ 3 = 1.…”

Section: Robustness To Contour Initializationsmentioning

confidence: 99%

“…X. Yang [25] proposed a shape-initialized and intensity-adaptive level set method (SIIALSM) to extract auroral oval boundaries. Research by P. Yang [26] utilized the distance constraint of inner and outer boundaries in the local information based dual level set method (LIDLSM) to determine the auroral oval regions. However, these two level set based methods are sensitive to the image inhomogeneity and evolving curve initializations.…”

Section: Introductionmentioning

confidence: 99%

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UVI Image Segmentation of Auroral Oval: Dual Level Set and Convolutional Neural Network Based Approach

Tian

Yang

et al. 2020

Applied Sciences

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The auroral ovals around the Earth’s magnetic poles are produced by the collisions between energetic particles precipitating from solar wind and atoms or molecules in the upper atmosphere. The morphology of auroral oval acts as an important mirror reflecting the solar wind-magnetosphere-ionosphere coupling process and its intrinsic mechanism. However, the classical level set based segmentation methods often fail to extract an accurate auroral oval from the ultraviolet imager (UVI) image with intensity inhomogeneity. The existing methods designed specifically for auroral oval extraction are extremely sensitive to the contour initializations. In this paper, a novel deep feature-based adaptive level set model (DFALS) is proposed to tackle these issues. First, we extract the deep feature from the UVI image with the newly designed convolutional neural network (CNN). Second, with the deep feature, the global energy term and the adaptive time-step are constructed and incorporated into the local information based dual level set auroral oval segmentation method (LIDLSM). Third, we extract the contour of the auroral oval through the minimization of the proposed energy functional. The experiments on the UVI image data set validate the strong robustness of DFALS to different contour initializations. In addition, with the help of deep feature-based global energy term, the proposed method also obtains higher segmentation accuracy in comparison with the state-of-the-art level set based methods.

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Section: Spflif-ismentioning

confidence: 99%

Section: Spflif-ismentioning

confidence: 99%

Section: Spflif-ismentioning

confidence: 99%

Section: Robustness To Contour Initializationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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UVI Image Segmentation of Auroral Oval: Dual Level Set and Convolutional Neural Network Based Approach

Tian

Yang

et al. 2020

Applied Sciences

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Automatic Auroral Boundary Determination Algorithm With Deep Feature and Dual Level Set

Tian

Yang

et al. 2020

JGR Space Physics

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The morphology of the auroral oval is an important geophysical parameter that helps to further understand the solar wind-magnetosphere-ionosphere coupling process. However, it is still a challenging task to automatically obtain auroral poleward and equatorward boundaries completely and accurately. In this paper, a new model based on the deep feature and dual level set method is proposed to extract the auroral oval boundaries in the images acquired by the Ultraviolet Imager (UVI) onboard the Polar spacecraft. With the deep feature extracted by the convolutional neural network (CNN), the corresponding deep feature energy functional is constructed and incorporated into the variational segmentation framework. The dual level set method is implemented to extract the accurate poleward and equatorward boundaries with the gradient descent flow. The experimental results on the test data set demonstrate that this model can extract complete auroral oval contours that are consistent well with annotations and owns higher accuracy compared with the previously proposed methods. Comparison between the extracted auroral boundaries and the precipitating boundaries determined by Defense Meteorological Satellite Program (DMSP) SSJ precipitating particle data validates that the proposed method is trustworthy to capture the global morphology of the auroral ovals.

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