Dual flow transformer network for multispectral image segmentation of wheat yellow rust

Zhifang Yang; Jiangyun Li; Xuefei Shi; Zhiyong Xu

doi:10.1117/12.2640930

2 December 2022 Dual flow transformer network for multispectral image segmentation of wheat yellow rust

Zhifang Yang, Jiangyun Li, Xuefei Shi, Zhiyong Xu

Proceedings Volume 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022); 122880K (2022) https://doi.org/10.1117/12.2640930
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 2022, Zhuhai, China

Abstract

Yellow rust is widely regarded as one of the most significant hazards faced by wheat. Traditional prevention methods are mainly based on pesticide application, which has high prevention costs and is harmful to the environment. Early disease detection and management can significantly reduce the cost of disease prevention and the impact on the environment. Therefore, a stable and reliable yellow rust detection method has become a direction for researchers. Spectral technology and deep learning methods are being introduced by more and more people to detect wheat yellow rust. In the recently years, Convolutional Neural Network (CNN) has got specific achievements of remote sensing image (RSI) processing. However, although CNN has achieved excellent performance, due to the locality of the convolution operation, it cannot learn the global and long-range semantic information interaction well. In this paper, we propose a dual flow transformer network (DF-Transformer), a combination of transformer and convolution, for multispectral image segmentation of wheat yellow rust. Tokenized image patches are delivered to a transformer-based encoder-decoder architecture with Unet blocks for global and long-range semantic feature learning. In particular, it uses a shifted window as an encoder, a maximum pooling-based U-Net block, and a hierarchical Swin transformer that extracts context functions. Next, an upconvolution-based decoder is designed to recover the spatial resolution of the feature maps. Experiments on multispectral images segmentation of wheat yellow rust demonstrate that the dual flow transformer network outperforms those methods with full-convolution.

Citation Download Citation

Zhifang Yang, Jiangyun Li, Xuefei Shi, and Zhiyong Xu "Dual flow transformer network for multispectral image segmentation of wheat yellow rust", Proc. SPIE 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 122880K (2 December 2022); https://doi.org/10.1117/12.2640930

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KEYWORDS

Transformers

Image segmentation

Computer programming

Multispectral imaging

Performance modeling

Convolution

Image processing

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