人工智能用于遥感目标可靠性识别:总体框架设计, 现状分析及展望

Translated title of the contribution: Artificial intelligence for reliable object recognition from remotely sensed data: overall framework design, review and prospect

Wenzhong Shi, Min Zhang

Research output: Journal article publicationReview articleAcademic researchpeer-review

Abstract

Reliability is one of the important features in remotely sensed data-based land use monitoring. Artificial intelligence (AI) technology promotes the rapid development of object recognition from remotely sensed data. However, the un-explainability in such image processing causes reliability problems. Based on the reliability theory and the basic theory of AI, this paper first presents the idea and the overall framework of intelligent and reliable object recognition. Second, the core research directions, including analysis of influencing factors, improvement methods, evaluation methods, and process control for reliability are sequentially introduced. Finally, the future development trend of AI for reliable object recognition from remotely sensed data is outlined.

Translated title of the contributionArtificial intelligence for reliable object recognition from remotely sensed data: overall framework design, review and prospect
Original languageChinese (Simplified)
Pages (from-to)1049-1058
Number of pages10
JournalCehui Xuebao/Acta Geodaetica et Cartographica Sinica
Volume50
Issue number8
DOIs
Publication statusPublished - Aug 2021

Keywords

  • Artificial intelligence
  • Object recognition
  • Reliability
  • Remote sensing

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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