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.
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.
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