Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 57, Issue 10, Pages 7492-7502Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2019.2913816
Keywords
Frequency domain; joint representation; remote sensing image classification; robust; space domain
Categories
Funding
- National Natural Science Foundation of China [61772510, 61702498]
- Key Research Program of Frontier Sciences, CAS [QYZDY-SSW-JSC044]
- Young Top-notch Talent Program of Chinese Academy of Sciences [QYZDB-SSW-JSC015]
- National Key Research and Development Program of China [2017YFB0502900]
- Chinese Academy of Sciences (CAS) Light of West China Program [XAB2017B15]
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Remote sensing image scene classification is a fundamental problem, which aims to label an image with a specific semantic category automatically. Recent progress on remote sensing image scene classification is substantial, benefitting mostly from the powerful feature extraction capability of convolutional neural networks (CNNs). Even though these CNN-based methods have achieved competitive performances, they only construct the representation of the image in location-sensitive space-domain. As a result, their representations are not robust to rotation-variant remote sensing images, which influence the classification accuracy. In this paper, we propose a novel feature representation method by introducing a frequency-domain branch to the traditional only-space-domain architecture. Our framework takes full advantages of discriminative features from space domain and location-robust features from the frequency domain, providing more advanced representations through an additional joint learning module, a property that is critically needed to perform remote sensing image scene classification. Additionally, our method produces satisfactory performances on four public and challenging remote sensing image scene data sets, Sydney, UC-Merced, WHU-RS19, and AID.
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