Deep learning for processing and analysis of remote sensing big data: a technical review

ABSTRACT In recent years, the rapid development of Earth observation technology has produced an increasing growth in remote sensing big data, posing serious challenges for effective and efficient processing and analysis. Meanwhile, there has been a massive rise in deep-learning-based algorithms for remote sensing tasks, providing a large opportunity for remote sensing big data. In this article, we initially summarize the features of remote sensing big data. Subsequently, following the pipeline of remote sensing tasks, a detailed and technical review is conducted to discuss how deep learning has been applied to the processing and analysis of remote sensing data, including geometric and radiometric processing, cloud masking, data fusion, object detection and extraction, land-use/cover classification, change detection and multitemporal analysis. Finally, we discussed technical challenges and concluded directions for future research in deep-learning-based applications for remote sensing big data.

• Publication year

  2021

• Venue

  Big Earth Data

• Publication date

  2021-08-30

• Fields of study

  Computer Science, Environmental Science

• Identifiers
  DOI 10.1080/20964471.2021.1964879
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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