A Survey of Semantic Construction and Application of Satellite Remote Sensing Images and Data

Lu, Hui; Liu, Qi; Liu, Xiaodong; Zhang, Yonghong

doi:10.4018/joeuc.20211101.oa6

Cited by 22 publications

(17 citation statements)

References 53 publications

(40 reference statements)

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“…e selection of traditional correlation filter-based individual objective pursuit algorithms is presented in this chapter and compared with deep learning-based individual objective pursuit algorithms, as shown in Table 1 [22]. Among them, MDnet is far ahead of other algorithms in terms of success rate and accuracy rate.…”

Section: Tracking Algorithm Dataset Evaluation Experimentsmentioning

confidence: 99%

Skill Movement Trajectory Recognition of Freestyle Skiing U-Shaped Field Based on Deep Learning and Multitarget Tracking Algorithm

Song

Zhao

2022

Computational Intelligence and Neuroscience

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Freestyle skiing U-shaped field is a snow sport that uses double boards to perform a series of action skills in a U-shaped pool, which requires very high skills for athletes. In this era of deep learning, in order to develop a more scientific training method, this paper combines multitarget tracking algorithm and deep learning to conduct research in freestyle skiing U-shaped venue skills motion capture. Therefore, this paper combines the convolutional neural network and multitarget tracking algorithm in deep learning to study the human action recognition technology, and then uses the LSTM module to study the freestyle skiing U-shaped venue skills. Finally, this paper designs the training method of the action recognition algorithm of the freestyle U-shaped skiing skills multitarget tracking algorithm based on deep learning. This paper also designs multitarget tracking dataset experiments and model updating experiments. Based on the data of experimental analysis, the training method designed in this paper is optimized, and finally compared with the traditional training method. Compared with the traditional freestyle U-shaped skiing skills training method, the experimental results show that the training method of the freestyle U-shaped skiing skills multitarget tracking algorithm action recognition algorithm is based on deep learning designed in this paper and this improves the skill score by 14.48%. Most professional students are very satisfied with the training method designed in this paper.

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Section: Tracking Algorithm Dataset Evaluation Experimentsmentioning

confidence: 99%

Skill Movement Trajectory Recognition of Freestyle Skiing U-Shaped Field Based on Deep Learning and Multitarget Tracking Algorithm

Song

Zhao

2022

Computational Intelligence and Neuroscience

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“…Deep learning has been widely used in data mining, machine learning, natural language processing, and speech and image recognition. Compared with the staged learning of traditional algorithms in computer vision, the end-to-end learning framework is a significant innovation of deep learning technology [10][11][12]. Under the framework of deep learning algorithm, it does not need to manually design features to perform feature extraction operations.…”

Section: Related Workmentioning

confidence: 99%

Wushu Routine Movement and Diagnosis Based on Deep Learning and Symmetric Difference Algorithm

Yan

Huang

2022

Computational Intelligence and Neuroscience

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Wushu is one of the traditional cultural symbols of the Chinese nation. It is also one of the most popular sports activities among the people. With the attention and love of contemporary people to sports activities, Wushu is also constantly developing and innovating. The requirements for professional martial arts routines of martial arts athletes are higher than ever. The development of martial arts has also made martial arts competitions more intense, and often a small detail of martial arts movements can determine the success or failure of the competition. Therefore, various Wushu teams pay more and more attention to the analysis and diagnosis of Wushu routines. It ensures that coaches and athletes can obtain more quantitative indicators of technical movement training. The analysis and diagnosis of martial arts routines are inseparable from the support of reliable science and technology and related algorithms. This article aims to study the analysis and diagnosis of martial arts routines based on deep learning and symmetric difference algorithm. It combines deep learning and symmetric difference algorithm to analyze and diagnose martial arts routines. The article concludes that the level of martial arts routines of martial arts athletes has the greatest influence on their martial arts competition performance, and its comprehensive influence index is as high as 4.3.

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“…The fully convolutional neural network (FCN) [51] has achieved good results. It adopts the skip architecture to combine semantic information from a deep, coarse layer with appearance information from a shallow, fine layer in order to produce accurate and detailed segmentations.…”

Section: Comparison Methodsmentioning

confidence: 99%

Red Tide Detection Method for HY−1D Coastal Zone Imager Based on U−Net Convolutional Neural Network

Zhao

Liu

et al. 2021

Remote Sensing

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Existing red tide detection methods have mainly been developed for ocean color satellite data with low spatial resolution and high spectral resolution. Higher spatial resolution satellite images are required for red tides with fine scale and scattered distribution. However, red tide detection methods for ocean color satellite data cannot be directly applied to medium–high spatial resolution satellite data owing to the shortage of red tide responsive bands. Therefore, a new red tide detection method for medium–high spatial resolution satellite data is required. This study proposes the red tide detection U−Net (RDU−Net) model by considering the HY−1D Coastal Zone Imager (HY−1D CZI) as an example. RDU−Net employs the channel attention model to derive the inter−channel relationship of red tide information in order to reduce the influence of the marine environment on red tide detection. Moreover, the boundary and binary cross entropy (BBCE) loss function, which incorporates the boundary loss, is used to obtain clear and accurate red tide boundaries. In addition, a multi−feature dataset including the HY−1D CZI radiance and Normalized Difference Vegetation Index (NDVI) is employed to enhance the spectral difference between red tides and seawater and thus improve the accuracy of red tide detection. Experimental results show that RDU−Net can detect red tides accurately without a precedent threshold. Precision and Recall of 87.47% and 86.62%, respectively, are achieved, while the F1−score and Kappa are 0.87. Compared with the existing method, the F1−score is improved by 0.07–0.21. Furthermore, the proposed method can detect red tides accurately even under interference from clouds and fog, and it shows good performance in the case of red tide edges and scattered distribution areas. Moreover, it shows good applicability and can be successfully applied to other satellite data with high spatial resolution and large bandwidth, such as GF−1 Wide Field of View 2 (WFV2) images.

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A Survey of Semantic Construction and Application of Satellite Remote Sensing Images and Data

Cited by 22 publications

References 53 publications

Skill Movement Trajectory Recognition of Freestyle Skiing U-Shaped Field Based on Deep Learning and Multitarget Tracking Algorithm

Skill Movement Trajectory Recognition of Freestyle Skiing U-Shaped Field Based on Deep Learning and Multitarget Tracking Algorithm

Wushu Routine Movement and Diagnosis Based on Deep Learning and Symmetric Difference Algorithm

Red Tide Detection Method for HY−1D Coastal Zone Imager Based on U−Net Convolutional Neural Network

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