DiRS: On Creating Benchmark Datasets for Remote Sensing Image Interpretation
release_42mdzmxpbbgenjcvhk6ka5pa5a
by
Yang Long, Gui-Song Xia, Shengyang Li, Wen Yang, Michael Ying Yang, Xiao Xiang Zhu, Liangpei Zhang, Deren Li
2020
Abstract
The past decade has 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, where benchmark datasets are 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 analysis. Specifically, we
first analyze the current challenges of developing intelligent algorithms for
RS image interpretation with bibliometric investigations. We then present some
principles, i.e., diversity, richness, and scalability (called DiRS), on
constructing benchmark datasets in efficient manners. Following the DiRS
principles, we also provide an example on building datasets for RS image
classification, i.e., Million-AID, a new large-scale benchmark dataset
containing million instances for RS 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 paper will provide
RS community an overall perspective on constructing large-scale and practical
image datasets for further research, especially data-driven ones.
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