A parallel sea-land segmentation method based on improved UNet model

Xuebo Zhang; Min Jiang; Jinli Li

doi:10.1117/12.2684350

1 August 2023 A parallel sea-land segmentation method based on improved UNet model

Xuebo Zhang, Min Jiang, Jinli Li

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 127541L (2023) https://doi.org/10.1117/12.2684350
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

Pixel-level sea-land segmentation on high-resolution remote sensing images is a basic task in remote sensing applications and is of great significance for coastline extraction and near-shore marine target detection. This paper proposes an improved UNet model for sea-land segmentation on high-resolution remote sensing images. This model outperforms UNet++ and other models in sea-land segmentation accuracy when applied to the HRSC2016-SL dataset. Based on this model, a parallel sea-land segmentation processing algorithm was developed, whose parallel efficiency reached 49.5% on 16K*14K remote sensing images. To complete this study, a parallel processing system for sea-land segmentation was developed, which achieved distributed and single-machine multi-core parallel sea-land segmentation tasks on highresolution remote sensing images.

Citation Download Citation

Xuebo Zhang, Min Jiang, and Jinli Li "A parallel sea-land segmentation method based on improved UNet model", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 127541L (1 August 2023); https://doi.org/10.1117/12.2684350

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