期刊
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
卷 14, 期 -, 页码 4205-4230出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2021.3070368
关键词
Remote sensing; Benchmark testing; Task analysis; Earth; Semantics; Annotations; Machine learning algorithms; Annotation; benchmark datasets; Million Aerial Image Dataset (Million-AID); remote sensing (RS) image interpretation; scene classification
类别
资金
- National Natural Science Foundation of China (NSFC) [61922065, 61771350, 41820104006, 61871299, 92038301]
- Science and Technology Major Project of Hubei Province (Next-Generation AI Technologies) [2019AEA170]
- German Federal Ministry of Education, and Research (BMBF) of the International Future AI Lab AI4EO -Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics, and Beyond
This article discusses how to efficiently prepare a suitable benchmark dataset for remote sensing (RS) image interpretation, presenting general guidances on creating benchmark datasets and providing an example of a Million Aerial Image Dataset. It also addresses challenges and perspectives in RS image annotation to facilitate research in benchmark dataset construction.
The past years have witnessed great progress on remote sensing (RS) image interpretation and its wide applications. With RS images becoming more accessible than ever before, there is an increasing demand for the automatic interpretation of these images. In this context, the benchmark datasets serve as an essential prerequisites for developing and testing intelligent interpretation algorithms. After reviewing existing benchmark datasets in the research community of RS image interpretation, this article discusses the problem of how to efficiently prepare a suitable benchmark dataset for RS image interpretation. Specifically, we first analyze the current challenges of developing intelligent algorithms for RS image interpretation with bibliometric investigations. We then present the general guidances on creating benchmark datasets in efficient manners. Following the presented guidances, we also provide an example on building RS image dataset, i.e., Million Aerial Image Dataset (Online. Available: https://captain-whu.github.io/DiRS/0), a new large-scale benchmark dataset containing a million instances for RS image scene classification. Several challenges and perspectives in RS image annotation are finally discussed to facilitate the research in benchmark dataset construction. We do hope this article will provide the RS community an overall perspective on constructing large-scale and practical image datasets for further research, especially data-driven ones.
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