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

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

Abstract

With the rapid development of satellite technology, remote sensing data has entered the era of big data, and the intelligent processing of remote sensing image has been paid more and more attention. Through the semantic research of remote sensing data, the processing ability of remote sensing data is greatly improved. This paper aims to introduce and analyze the research and application progress of remote sensing image satellite data processing from the perspective of semantic. Firstly, it introduces the characteristics and semantic knowledge of remote sensing big data; Secondly, the semantic concept, semantic construction and application fields are introduced in detail; then, for remote sensing big data, the technical progress in the study field of semantic construction is analyzed from four aspects: semantic description and understanding, semantic segmentation, semantic classification and semantic search, focusing on deep learning technology; Finally, the problems and challenges in the four aspects are discussed in detail, in order to find more directions to explore.

Citation

Lu, H., Liu, Q., Liu, X., & Zhang, Y. (2021). A Survey of Semantic Construction and Application of Satellite Remote Sensing Images and Data. Journal of Organizational and End User Computing, 33(6), Article 6. https://doi.org/10.4018/joeuc.20211101.oa6

• Journal Article Type: Article
• Publication Date: 2021-11
• Deposit Date: Aug 5, 2021
• Publicly Available Date: Aug 5, 2021
• Journal: Journal of Organizational and End User Computing
• Print ISSN: 1546-2234
• Electronic ISSN: 1546-5012
• Publisher: IGI Global
• Peer Reviewed: Peer Reviewed
• Volume: 33
• Issue: 6
• Article Number: 6
• DOI: https://doi.org/10.4018/joeuc.20211101.oa6
• Keywords: Automatic Analysis, Deep Learning, Remote Sensing, Satellite Remote Sensing, Semantic Construction, Semantic Knowledge
• Public URL: http://researchrepository.napier.ac.uk/Output/2791401