Research on parallel technology of sea and land segmentation based on deep learning

Min Jiang; Xuebo Zhang; Chengguang Zhang

doi:10.1117/12.2623443

16 February 2022 Research on parallel technology of sea and land segmentation based on deep learning

Min Jiang, Xuebo Zhang, Chengguang Zhang

Proceedings Volume 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021); 120831N (2022) https://doi.org/10.1117/12.2623443
Event: Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 2021, Kunming, China

Abstract

Based on the requirements of large-scale and high-resolution remote sensing image data processing, this paper proposes a distributed parallel processing model based on sea and land segmentation tasks. Based on the trained DeepUnet model, the mpi4py function library is used for parallel algorithm design to realize multi-process synchronization processing. Increase the number of processors and reduce the processing time of large-scale and high-resolution remote sensing image data. The experimental results show that on the basis of ensuring the detection accuracy, the parallel sea-land segmentation technology can significantly shorten the image processing time compared with the traditional serial sea-land segmentation technology, and has strong scalability.

Citation Download Citation

Min Jiang, Xuebo Zhang, and Chengguang Zhang "Research on parallel technology of sea and land segmentation based on deep learning", Proc. SPIE 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 120831N (16 February 2022); https://doi.org/10.1117/12.2623443

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