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TGF-Net: Transformer and gist CNN fusion network for multi-modal remote sensing image classification

I tiakina i:
I whakaputaina i:PLoS One vol. 20, no. 2 (Feb 2025), p. e0316900
Kaituhi matua: Wang, Huiqing
Ētahi atu kaituhi: Wang, Huajun, Wu, Linfen
I whakaputaina:
Public Library of Science
Ngā marau:
Feature extraction
Accuracy
Datasets
Collaboration
Deep learning
Artificial neural networks
Neural networks
Remote sensing
Classification
Modules
Radar imaging
Machine learning
Synthetic aperture radar
Image reconstruction
Earth sciences
Image classification
Integrated approach
Methods
Morphology
Hyperspectral imaging
Data visualization
Environmental
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Whakarāpopotonga: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.
ISSN:1932-6203
DOI:10.1371/journal.pone.0316900
Puna:Health & Medical Collection