测绘通报 ›› 2024, Vol. 0 ›› Issue (4): 124-128,134.doi: 10.13474/j.cnki.11-2246.2024.0421

• 技术交流 • 上一篇    

基于深度学习的国产卫片图斑提取

庞敏   

  1. 山西省测绘地理信息院, 山西 太原 030001
  • 收稿日期:2023-12-21 发布日期:2024-04-29
  • 作者简介:庞敏(1982—),女,硕士,主要研究方向为地理信息系统与遥感图像处理。E-mail:78211684@qq.com
  • 基金资助:
    山西省重点研发计划(202202010101005)

Extraction of domestic satellite images patches based on deep learning

PANG Min   

  1. Shanxi Institute of Surveying, Mapping and Geographic Information, Taiyuan 030001, China
  • Received:2023-12-21 Published:2024-04-29

摘要: 本文针对国产卫片多时相、长时序、全天候、多源海量等特点,提出了一种高效、准确的卫片图斑提取方法。该方法在深度学习理论基础上构建了地物目标语义分割模型和图斑提取智能算法群,实现了国产卫片图斑的特征、规律及属性的自动识别,完成了卫片图斑提取的智能化和自动化。试验结果表明,该方法在国产卫片图斑提取中具有较高的准确率,为后续图像处理、分析和应用提供了重要支持。

关键词: 深度学习, 国产卫片, 图斑提取

Abstract: This paper addresses the characteristics of domestic satellite imagery,such as multi-temporal,long-time series,massive,and massive multi-source data,proposes an efficient and accurate method for the extraction of satellite imagery patches. Based on the principles of deep learning,this method constructs a semantic segmentation model for ground objects and a group of intelligent algorithms for patch extraction based on deep learning theory,enabling the automatic recognition of the features,patterns,and attributes of satellite imagery patches,which leads to the intelligent and automated extraction of these patches. Experimental results demonstrate that this method achieves a high level of accuracy in the extraction of patches from domestic satellite imagery,provides important support for subsequent image processing,analysis,and applications.

Key words: deep learning, domestic satellite images, spot extraction

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