Comparing different thresholding algorithms for segmenting auroras

Li, X.; Ramachandran, Rahul; He, Maowei; Movva, Sunil; Rushing, John; Graves, Sara; Lyatsky, W.; Tan, A.

doi:10.1109/itcc.2004.1286718

Cited by 11 publications

(3 citation statements)

References 15 publications

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“…Various segmentation algorithms have been used to make it more precise and efficient. Early segmentation methods, such as the pulse‐coupled neural network (Germany et al., 1998), histogram‐based k‐means (Hung & Germany, 2003), and adaptive minimum error thresholding (Li et al., 2004), were based on the grayscale and pixel space of images. However, since no prior knowledge of aurora shape was utilized, these methods were easily disturbed by noise.…”

Section: Introductionmentioning

confidence: 99%

A Superposed Epoch Analysis of Auroral Oval Coverage During Substorms Using Deep Learning‐Based Segmentation Models

Jiang,

Zou,

et al. 2024

Space Weather

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As an effect of solar‐terrestrial activity, the aurora has always been a focus of substorm research. The expansion of the aurora is one of the important characteristics during substorms, which could be directly reflected by the change of auroral oval coverage. However, the related studies were limited because of the absence of a reliable automated method to obtain auroral ovals from a large number of images. In this paper, we propose a new strategy to achieve this. Based on the Segment Anything Model, we design a process for annotating the auroral oval region that requires little manual work. A new aurora segmentation model, HrSeg, is then developed to obtain auroral ovals more efficiently and accurately. Through 5‐fold cross‐validation, it is determined that the average intersection over union, Dice coefficient, and pixel accuracy are all greater than 0.97. Furthermore, images of 590 substorms observed by the Polar satellite Ultraviolet Imager are segmented. We present superposed epoch analyses of the auroral oval coverage calculated from the segmentation results. Generally, the coverage decreases slightly before onset, then rapidly increases for tens of minutes after onset, and finally decreases gradually. Moreover, the auroras in different magnetic local time (MLT) sectors exhibit different evolutions in coverage. It is also revealed that the evolution pattern of auroral coverage depends on interplanetary magnetic field orientations and seasonal conditions. The results quantify the variation of auroral morphology in terms of coverage, which complete the evolution pattern of aurora during substorms and provide a more comprehensive understanding of substorms.

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Section: Introductionmentioning

confidence: 99%

A Superposed Epoch Analysis of Auroral Oval Coverage During Substorms Using Deep Learning‐Based Segmentation Models

Jiang,

Zou,

et al. 2024

Space Weather

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“…Existing auroral oval segmentation methods can be broadly classified into two categories, active contour-free and active contour-based models [17]. Active contour-free models include adaptive minimum error thresholding (AMET) [18], linear randomized Hough transform (LLSRHT) [19], maximal similarity-based region merging (MSRM) [20], quasi-elliptical fitting with fuzzy local information C-means clustering result (FCM + QEF) [8], etc. As a pixel-based method, the AMET model cannot obtain a complete auroral oval in the low contrast region.…”

Section: Introductionmentioning

confidence: 99%

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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“…[11] compared several well-known thresholding algorithms for their performances in the automated auroral oval detection. It was found that the Chow and Kaneko (CK) algorithm [12] performed best among the compared algorithms.…”

Section: B Removal Of Dayglow Effectmentioning

confidence: 99%

Dayglow removal from FUV auroral images

Ramachandran

Movva

et al.

IEEE International IEEE International IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings

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Aurora study is a key area in understanding the connection and interaction of the solar-terrestrial system. Auroral events are monitored on the global scale at the Far Ultraviolet (FUV) spectrum by satellite-based sensors. However, the existence of dayglow emission significantly limits scientists is the ability to determine the location and the size of auroral ovals. A dynamic methodology to remove day airglow emission from the LBHL band UVI images on the Polar satellite is presented in this paper. First, the methodology identifies the geomagnetic latitude bound of the night side auroral oval. Then, the maximum range of geomagnetic latitude bound of the auroral is inferred based on the domain knowledge. Using the non-auroral dayglow pixels, a multi-variable regression fit of the intensity as function of the cosine of the solar zenith angle and cosine of the satellite viewing zenith angle is obtained. The methodology then uses this fitting function to estimate the dayglow intensity and to remove its effect in the LBHL band FUV images. This methodology does not require an external source of input solar flux and the dayglow removal is solely based on individual images. Experiment results show that dayglow effect is removed significantly from the original LBHL FUV images.Using this methodology, the performance of auroral oval detection algorithms is significantly improved.

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Comparing different thresholding algorithms for segmenting auroras

Cited by 11 publications

References 15 publications

A Superposed Epoch Analysis of Auroral Oval Coverage During Substorms Using Deep Learning‐Based Segmentation Models

A Superposed Epoch Analysis of Auroral Oval Coverage During Substorms Using Deep Learning‐Based Segmentation Models

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

Dayglow removal from FUV auroral images

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