Journal
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
Volume 145, Issue -, Pages 197-209Publisher
ELSEVIER
DOI: 10.1016/j.isprsjprs.2018.01.004
Keywords
Remote sensing; Content based image retrieval (CBIR); Benchmark dataset; Handcrafted features; Deep learning; Convolutional neural networks
Categories
Funding
- National Key Technologies Research and Development Program [2016YFB0502603]
- Fundamental Research Funds for the Central Universities [20420161d0179, 2042016kf1019]
- Wuhan Chen Guang Project [2016070204010114]
- Guangzhou science and technology project [201604020070]
- Special task of technical innovation in Hubei Province [2016AAA018]
- National Natural Science Foundation of China [61671332, 41771452, 41771454]
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Benchmark datasets are critical for developing, evaluating, and comparing remote sensing image retrieval (RSIR) approaches. However, current benchmark datasets are deficient in that (1) they were originally collected for land use/land cover classification instead of RSIR; (2) they are relatively small in terms of the number of classes as well as the number of images per class which makes them unsuitable for developing deep learning based approaches; and (3) they are not appropriate for RSIR due to the large amount of background present in the images. These limitations restrict the development of novel approaches for RSIR, particularly those based on deep learning which require large amounts of training data. We therefore present a new large-scale remote sensing dataset termed PatternNet that was collected specifically for RSIR. PatternNet was collected from high-resolution imagery and contains 38 classes with 800 images per class. Significantly, PatternNet's large scale makes it suitable for developing novel, deep learning based approaches for RSIR. We use PatternNet to evaluate the performance of over 35 RSIR methods ranging from traditional handcrafted feature based methods to recent, deep learning based ones. These results serve as a baseline for future research on RSIR. (C) 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
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