Bulletin of Surveying and Mapping ›› 2024, Vol. 0 ›› Issue (10): 84-90.doi: 10.13474/j.cnki.11-2246.2024.1014.

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Large format remote sensing image segmentation method based on Spark with optimised chunking

XIE Zhiwei1,2,4, SONG Guangming2, ZHANG Fengyuan3, CHEN Min1,4, PENG Bo5   

  1. 1. Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210042, China;
    2. College of Transportation and Surveying Engineering, Shenyang Jianzhu University, Shenyang 110168, China;
    3. College of the Environment, Nanjing Normal University, Nanjing 210042, China;
    4. School of Geographic Sciences, Nanjing Normal University, Nanjing 210042, China;
    5. School of Surveying and Mapping Engineering, Liaoning Ecological Engineering Vocational College, Shenyang 110122, China
  • Received:2024-01-30 Published:2024-11-02

Abstract: Aiming at the large format remote sensing image in the chunk boundary feature discontinuity and segmentation efficiency is not high. In this paper, we propose a simple linear iterative clustering superpixel segmentation algorithm (SLIC) that combines the Spark platform and optimal compactness evaluation. Firstly, the SLIC superpixel segmentation method with optimal tightness is used to complete the image chunking, which solves the problem of low accuracy of the chunk boundary; then, the SLIC segmentation algorithm is used in parallel to the chunked data by using Spark to improve the computational efficiency; finally, the SLIC algorithm is improved by using the ratio of vegetation index combined with the method of maximum interclass variation to improve the accuracy of the superpixel segmentation.WorldView-2 Satellite Imagery and GF-2 images are used as experimental data. The experimental results show that the improved SLIC method improves about 9 times of the original method in terms of computing efficiency, 1.5% of the edge fitting precision, 8.2% of the under-segmentation error, and 0.2% of the edge recall.

Key words: large format remote sensing image, Spark platform, improved SLIC algorithm, parallel computing, optimal parameter evaluation

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