Study on the Satellite Telemetry Data Classification Based on Self-Learning

Wan, Peng; Zhan, Yafeng; Jiang, Weiwei

doi:10.1109/access.2019.2962235

Cited by 13 publications

(9 citation statements)

References 22 publications

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“…To classify satellite image datasets and samples, a variety of models, including CNNs, SVMs, DTs, DBNs, and ensemble models, are employed. Every model has advantages and disadvantages, and the selection of a model depends on the needs of the specific application [30,31,32]. Recent research has demonstrated that deep learning models such as CNNs and DBNs outperform more conventional machine learning models such as SVMs and DTs in satellite image classification tasks [33,34,35].…”

Section: Literature Reviewmentioning

confidence: 99%

MSDMCCG: Design of an efficient Multimodal Satellite Data Processing Model for Component-level analysis of Contextual Geographic entities

Bhoi,

Patel

2023

Preprint

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This paper presents an efficient model for analysing contextual geographic entities in satellite data at a component level. The model utilizes various feature extraction techniques, including Fourier, Entropy, Wavelet, and Gabor, along with vegetation indices such as NDVI, SAVI, and EVI, to capture the multimodal characteristics of the satellite data. Classification operations are performed using a binary cascaded convolutional neural network (CNN), while incremental learning is facilitated through the Q-learning process. The proposed model offers a novel approach by combining different feature extraction methods, allowing for a comprehensive representation of satellite data. It employs the BCCNN, which exhibits high accuracy in identifying specific geographical features like land, forests, structures, and rivers. Additionally, by utilizing Q-learning for incremental learning, the model's accuracy can improve over time as new data becomes available. Evaluation of the model using an augmented cluster of datasets and samples demonstrated its ability to accurately identify contextual geographic entities with 99.5% accuracy, 98.5% precision, and 98.3% recall. This model holds promise for various applications, including environmental monitoring, disaster management, and urban planning.

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Section: Literature Reviewmentioning

confidence: 99%

MSDMCCG: Design of an efficient Multimodal Satellite Data Processing Model for Component-level analysis of Contextual Geographic entities

Bhoi,

Patel

2023

Preprint

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“…In their study of the use of AI for fault diagnosis in general and for space utilization, Sun et al [125] argued that the most promising direction is the use of DL; suggested its usage for fault diagnosis for space utilization in China. By comparing different ML algorithms using telemetry data from the Egyptsat-1 satellite, Ibrahim et al [126] demonstrated the high prediction accuracy of LSTM, ARIMA, and RNN models. They suggested simple linear regression for forecasting critical satellite features for short-lifetime satellites (i.e., 3-5 years) and NNs for long-lifetime satellites (15-20 years).…”

Section: Ai-based Solutionsmentioning

confidence: 99%

Artificial intelligence for satellite communication: A review

Fourati

Alouini

2021

Intell. and Converged Netw.

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Satellite communication offers the prospect of service continuity over uncovered and under-covered areas, service ubiquity, and service scalability. However, several challenges must first be addressed to realize these benefits, as the resource management, network control, network security, spectrum management, and energy usage of satellite networks are more challenging than that of terrestrial networks. Meanwhile, artificial intelligence (AI), including machine learning, deep learning, and reinforcement learning, has been steadily growing as a research field and has shown successful results in diverse applications, including wireless communication. In particular, the application of AI to a wide variety of satellite communication aspects has demonstrated excellent potential, including beam-hopping, anti-jamming, network traffic forecasting, channel modeling, telemetry mining, ionospheric scintillation detecting, interference managing, remote sensing, behavior modeling, space-air-ground integrating, and energy managing. This work thus provides a general overview of AI, its diverse sub-fields, and its state-of-the-art algorithms. Several challenges facing diverse aspects of satellite communication systems are then discussed, and their proposed and potential AI-based solutions are presented. Finally, an outlook of field is drawn, and future steps are suggested.

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“…In recent years, AI and ML techniques have been largely considered in solving challenges related to satellite communications such as beam hopping [158], channel modeling [159], and telemetry mining [160]. Employing FL in satellite-aerialterrestrial networks is still in its infancy; thus, there is plenty of room to explore the potential of FL in such networks.…”

Section: ) Cell-free Massive Mimo (Cfmmimo)mentioning

confidence: 99%

Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges

Al-Quraan¹,

Mohjazi²,

Bariah³

et al. 2021

Preprint

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The unprecedented surge of data volume in wireless networks empowered with artificial intelligence (AI) opens up new horizons for providing ubiquitous data-driven intelligent services. Traditional cloud-centric machine learning (ML)-based services are implemented by collecting datasets and training models centrally. However, this conventional training technique encompasses two challenges: (i) high communication and energy cost due to increased data communication, (ii) threatened data privacy by allowing untrusted parties to utilise this information. Recently, in light of these limitations, a new emerging technique, coined as federated learning (FL), arose to bring ML to the edge of wireless networks. FL can extract the benefits of data silos by training a global model in a distributed manner, orchestrated by the FL server. FL exploits both decentralised datasets and computing resources of participating clients to develop a generalised ML model without compromising data privacy. In this article, we introduce a comprehensive survey of the fundamentals and enabling technologies of FL. Moreover, an extensive study is presented detailing various applications of FL in wireless networks and highlighting their challenges and limitations. The efficacy of FL is further explored with emerging prospective beyond fifth generation (B5G) and sixth generation (6G) communication systems. The purpose of this survey is to provide an overview of the state-of-the-art of FL applications in key wireless technologies that will serve as a foundation to establish a firm understanding of the topic. Lastly, we offer a road forward for future research directions.

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Study on the Satellite Telemetry Data Classification Based on Self-Learning

Cited by 13 publications

References 22 publications

MSDMCCG: Design of an efficient Multimodal Satellite Data Processing Model for Component-level analysis of Contextual Geographic entities

MSDMCCG: Design of an efficient Multimodal Satellite Data Processing Model for Component-level analysis of Contextual Geographic entities

Artificial intelligence for satellite communication: A review

Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges

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