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 contribution | Artificial intelligence for reliable object recognition from remotely sensed data: overall framework design, review and prospect |
|---|---|
| Original language | Chinese (Simplified) |
| Pages (from-to) | 1049-1058 |
| Number of pages | 10 |
| Journal | Cehui Xuebao/Acta Geodaetica et Cartographica Sinica |
| Volume | 50 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - Aug 2021 |
Keywords
- Artificial intelligence
- Object recognition
- Reliability
- Remote sensing
ASJC Scopus subject areas
- General Earth and Planetary Sciences
