An FFT-based CNN-Transformer Encoder for Semantic Segmentation of Radar Sounder Signal
Proceedings of SPIE - The International Society for Optical Engineering, ISSN: 1996-756X, Vol: 12267
2022
- 2Citations
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations2
- Citation Indexes2
Conference Paper Description
Radar Sounders (RSs) are sensors operating in the nadir-looking geometry (with HF or VHF bands) by transmitting modulated electromagnetic (EM) pulses and receiving the backscattering response from different subsurface targets. Recently, convolutional neural network (CNN) architectures were established for characterizing RS signals under the semantic segmentation framework. In this paper, we design a Fast Fourier Transform (FFT) based CNN-Transformer encoder to effectively capture the long-range contexts in the radargram. In our hybrid architecture, CNN models the high-dimensional local spatial contexts, and the Transformer establishes the global spatial contexts between the local spatial ones. To overcome Transformer complex self-attention layers by reducing learnable parameters; - we replace the self-attention mechanism of the Transformer with unparameterized FFT modules as depicted in FNet architecture for Natural Language Processing (NLP). The experimental results on the MCoRDS dataset indicate the capability of the CNN-Transformer encoder along with the unparameterized FFT modules to characterize the radargram with limited accuracy cost and by reducing the time consumption. A comparative analysis is carried out with the state-of-the-art Transformer-based architecture.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85142475737&origin=inward; http://dx.doi.org/10.1117/12.2636693; https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12267/2636693/An-FFT-based-CNN-Transformer-Encoder-for-Semantic-Segmentation-of/10.1117/12.2636693.full; https://dx.doi.org/10.1117/12.2636693; https://www.spiedigitallibrary.org/access-suspended
SPIE-Intl Soc Optical Eng
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