TGF-Net: Transformer and gist CNN fusion network for multi-modal remote sensing image classification.
In the field of earth sciences and remote exploration, the classification and identification of surface materials on earth have been a significant research area that poses considerable challenges in recent times. Although deep learning technology has achieved certain results in remote sensing image...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0316900 |
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| Summary: | In the field of earth sciences and remote exploration, the classification and identification of surface materials on earth have been a significant research area that poses considerable challenges in recent times. Although deep learning technology has achieved certain results in remote sensing image classification, it still has certain challenges for multi-modality remote sensing data classification. In this paper, we propose a fusion network based on transformer and gist convolutional neural network (CNN), namely TGF-Net. To minimize the duplication of information in multimodal data, the TGF-Net network incorporates a feature reconstruction module (FRM) that employs matrix factorization and self-attention mechanism for decomposing and evaluating the similarity of multimodal features. This enables the extraction of distinct as well as common features. Meanwhile, the transformer-based spectral feature extraction module (TSFEM) was designed by combining the different characteristics of remote sensing images and considering the problem of orderliness of the sequence between hyperspectral image (HSI) channels. In order to address the issue of representing the relative positions of spatial targets in synthetic aperture radar (SAR) images, we proposed a spatial feature extraction module called gist-based spatial feature extraction module (GSFEM). To assess the efficacy and superiority of the proposed TGF-Net, we performed experiments on two datasets comprising HSI and SAR data. |
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| ISSN: | 1932-6203 |