ISPRS Journal of Photogrammetry and Remote Sensing· 2019Q1· Review
Deep learning in remote sensing applications: A meta-analysis and review
- 2,326citations
- Q1SCImago
- 2019year
Short summary
This meta-analysis and review synthesizes over 200 recent publications on deep learning (DL) applications in remote sensing, revealing trends in study targets, DL models, spatial resolutions, and classification accuracy.
AI-generated from the title and abstract; the full text is not read.
Key points
- Analyzed over 200 recent deep learning (DL) publications in remote sensing.
- Meta-analysis covers study targets, DL models, image resolutions, and classification accuracy.
- Detailed review discusses DL applications in image fusion, object detection, land cover classification, and more.
- Identifies current state-of-the-art methods and open challenges in the field.
AI-generated from the title and abstract; the full text is not read.
Abstract
Deep learning (DL) algorithms have seen a massive rise in popularity for remote-sensing image analysis over the past few years. In this study, the major DL concepts pertinent to remote-sensing are introduced, and more than 200 publications in this field, most of which were published during the last two years, are reviewed and analyzed. Initially, a meta-analysis was conducted to analyze the status of remote sensing DL studies in terms of the study targets, DL model(s) used, image spatial resolution(s), type of study area, and level of classification accuracy achieved. Subsequently, a detailed review is conducted to describe/discuss how DL has been applied for remote sensing image analysis tasks including image fusion, image registration, scene classification, object detection, land use and land cover (LULC) classification, segmentation, and object-based image analysis (OBIA). This review covers nearly every application and technology in the field of remote sensing, ranging from preprocessing to mapping. Finally, a conclusion regarding the current state-of-the art methods, a critical conclusion on open challenges, and directions for future research are presented.
The authors' abstract, as published at the source. ISPRS Journal of Photogrammetry and Remote Sensing, 2019 · DOI ↗
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Field: Media Technology
Media TechnologyEngineering