CFCANet: A Complete Frequency Channel Attention Network for SAR Image Scene Classification
An important means of remote sensing (RS) imagery interpretation, RS scene classification technology, has recently achieved great success, especially based on deep learning. However, most of these methods are designed for noise-free images. The scene classification performance for noisy RS images, i...
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| Autores principales: | , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/8f059e91f99948b581362a8e334dbc4b |
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| Sumario: | An important means of remote sensing (RS) imagery interpretation, RS scene classification technology, has recently achieved great success, especially based on deep learning. However, most of these methods are designed for noise-free images. The scene classification performance for noisy RS images, i.e., synthetic aperture radar (SAR) images with speckle noise, is poor due to the sufficient effect of noise. An intuitive solution is denoising first and then classifying the image, which makes the whole process cumbersome. To address this problem, we design a new complete frequency channel attention network (CFCANet) that can handle noisy RS images directly without any filtering operation. CFCANet selects part of the low-frequency information to interact with the feature map adequately. For the original feature map, a corresponding 2-D discrete cosine transformation frequency component is assigned, from which the most significant eigenvalue of each channel is obtained by maximization. Compared with the frequency channel attention network (FcaNet), the proposed network has better antinoise ability as it exploits low frequency information of the images. The effectiveness of our method has been proved by experiments based on public datasets and some simulated datasets. Moreover, we build a new SAR scene classification dataset: WHU-SAR6. The comprehensive evaluation shows that the proposed method consistently outperforms several advanced methods, including ResNet, SENet, CBAM, EcaNet, and FcaNet. |
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