Abstract
In order to keep the clear boundary contour for segmented object in semantic segmentation of remote sensing image of coastal wetlands, a deep learning semantic segmentation model called WetlandNet is proposed by improving UNet. The model takes encoder-decoder as its basic structure, uses depthwise separable convolution instead of regular convolution to reduce the model parameters, uses deconvolution to extract boundary contour features of the object, and connects these features to upsampled feature maps by jump connection. This paper takes the remote sensing image of the Yellow River Estuary wetland in Kenli District, Dongying City, Shandong Province, China, taken by GF-2 satellite as an example. The experimental results show that compared with advanced semantic segmentation models UNet, PSPNet and DeepLabV3 in deep learning, the proposed model achieves more accurate segmentation results, which improves the OA by more than 5%, Kappa by more than 0.07, and the F1 scores of five of the six classes are higher than those of contrast models, while the number of parameters is only 1/36 of UNet, 1/42 of PSPNet and 1/51 of DeepLabV3.
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Cui, B., Zhang, Y., Li, X., Wu, J., Lu, Y. (2020). WetlandNet: Semantic Segmentation for Remote Sensing Images of Coastal Wetlands via Improved UNet with Deconvolution. In: Pan, JS., Lin, JW., Liang, Y., Chu, SC. (eds) Genetic and Evolutionary Computing. ICGEC 2019. Advances in Intelligent Systems and Computing, vol 1107. Springer, Singapore. https://doi.org/10.1007/978-981-15-3308-2_32
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DOI: https://doi.org/10.1007/978-981-15-3308-2_32
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