First and second-order information fusion networks for remote sensing scene classification
Li, Erzhu; Samat, Alim; Zhang, Ce 
ORCID: https://orcid.org/0000-0001-5100-3584; Du, Peijun; Liu, Wei.
2022
First and second-order information fusion networks for remote sensing scene classification.
IEEE Geoscience and Remote Sensing Letters, 19, 8014405.
5, pp.
10.1109/LGRS.2021.3090045
Abstract/Summary
Deep convolutional networks have been the most competitive method in remote sensing scene classification. Due to the diversity and complexity of scene content, remote sensing scene classification still remains a challenging task. Recently, the second-order pooling method has attracted more interest because it can learn higher-order information and enhance the nonlinear modeling ability of the networks. However, how to effectively learn second-order features and establish the discriminative feature representation of holistic images is still an open question. In this letter, we propose a first and second-order information fusion network (FSoI-Net) that can learn the first-order and second-order features at the same time, and construct the final feature representation by fusing the two types of features. Specifically, a self-attention-based second-order pooling (SaSoP) method based on covariance matrix is proposed to extract second-order features, and a fusion loss function is developed to jointly train the model and construct the final feature representation for the classification decision. The proposed network has been thoroughly evaluated on three real remote sensing scene datasets and achieved better performance than the counterparts.
| Item Type: | Publication - Article |
|---|---|
| Digital Object Identifier (DOI): | 10.1109/LGRS.2021.3090045 |
| UKCEH and CEH Sections/Science Areas: | Soils and Land Use (Science Area 2017-) |
| ISSN: | 1545-598X |
| Additional Keywords: | deep learning, second-order pooling, self-attention mechanism, information fusion, scene classification, feature extraction, covariance matrices, remote sensing, convolution, task analysis, tensors, training |
| NORA Subject Terms: | Earth Sciences Data and Information |
| Date made live: | 01 Jan 2022 23:26 +0 (UTC) |
| URI: | https://nora.nerc.ac.uk/id/eprint/531664 |
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