期刊
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
卷 13, 期 -, 页码 5297-5313出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2020.3021045
关键词
Image analysis; Feature extraction; Remote sensing; Visualization; Semantics; Nonhomogeneous media; Machine learning; Diverse CapsNets; feature attention; feature fusion; remote sensing; scene classification
类别
资金
- National Key Research and Development Program of China [2018YFB0505000]
- National Science and Technology Major Project [21-Y20A06-9001-17/18]
- Science Fund for Creative Research Groups of the National Natural Science Foundation of China [61221003]
Remote sensing image scene classification has drawn significant attention for its potential applications in the economy and livelihoods. Unlike the traditional handcrafted features, the convolutional neural networks provide an excellent avenue in obtaining powerful discriminative features. Although tremendous efforts have been made so far in this domain, there are still many open challenges in scene classification due to the scene complexity with higher within-class diversity and between-class similarity. To solve the above-mentioned problems, DcapsulesNet (D-CapsNet) is proposed to learn the richer and more robust features for scene classification. It is an end to end network with four types of layers and incorporates visual attention mechanisms. Its diverse capsules encode different properties of complex image scenes, including deep high-level features, spatial attention based on the fusion of multilayers features, both spatial and channel attention based on high-level features, and their fusion. Experiments on three image scene datasets demonstrate that D-CapsNet outperforms other baselines and state-of-the-art methods with a significant improvement in both classification accuracy and speed.
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