地球信息科学学报

地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (10): 2051-2061.doi: 10.12082/dqxxkx.2020.200001

• 遥感科学与应用技术 • 上一篇    下一篇

深度学习和遥感影像支持的矢量图斑地类解译真实性检查方法

郭子慧1(), 刘伟1,2,*()   

  1. 1.江苏师范大学地理测绘与城乡规划学院,徐州 221116
    2.中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101
  • 收稿日期:2020-01-01 修回日期:2020-04-01 出版日期:2020-10-25 发布日期:2020-12-25
  • 通讯作者: 刘伟 E-mail:guozihui@jsnu.edu.cn;liuw@jsnu.edu.cn
  • 作者简介:郭子慧（1996— ）,女,江苏宿迁人,硕士生,主要从事空间数据质量检查,遥感图像分析处理研究。E-mail:guozihui@jsnu.edu.cn
  • 基金资助:
    江苏省研究生科研与实践创新计划项目(KYCX20_2373);江苏高校优势学科建设工程资助项目;徐州市国土资源科技项目(XZGTKJ2018001);江苏省国土资源科技计划项目(2018054);资源与环境信息系统国家重点实验 室开放基金项目

Land Type Interpretation Authenticity Check of Vector Patch Supported by Deep Learning and Remote Sensing Image

GUO Zihui1(), LIU Wei1,2,*()   

  1. 1. School of Geographic Mapping and Urban Rural Planning, Jiangsu Normal University, Xuzhou 221116, China
    2. State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciencesand Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • Received:2020-01-01 Revised:2020-04-01 Online:2020-10-25 Published:2020-12-25
  • Contact: LIU Wei E-mail:guozihui@jsnu.edu.cn;liuw@jsnu.edu.cn
  • Supported by:
    Postgraduate Research & Practice Innovation Program of Jiangsu Province(KYCX20_2373);A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions;Xuzhou Land and Resources Science and Technology Project(XZGTKJ2018001);Jiangsu Province Land and Resources Science and Technology Plan Project(2018054);Open Fund of National Key Laboratory of Resource and Environment Information System

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摘要：

空间数据质量检查是数据准确可靠的重要保障,是数据的生命线。然而,目前的空间数据质量检查主要针对拓扑关系、属性一致性以及数据间的相关性进行检查,往往忽视矢量图斑地类解译真实性问题。因此,本文提出深度学习和高分遥感影像支持的矢量图斑地类解译真实性检查方法,选用深度学习经典模型Inception_v3进行迁移学习,对分割后的影像进行自动场景分类,以高分遥感影像块的场景分类结果作为参照依据,对场景分类结果与矢量图斑原始数据进行叠加分析,自动查找出类别信息不符的分割单元,从而提取出可疑图斑,实现矢量图斑地类解译真实性自动检查,并在徐州市贾汪区青山泉镇和大吴镇的矢量图斑地类解译真实性检查中进行验证。实验结果表明,本文方法在研究区图斑地类解译真实性检查中的精确率和召回率分别高达0.925和0.817,可为矢量图斑地类解译真实性检查提供可靠的技术支撑。

关键词: 深度学习, Inception_v3, 高分遥感影像, 空间数据质量检查, 矢量图斑, 迁移学习, 场景分类, 真实性

Abstract:

Spatial data quality check is important to guarantee the accuracy and reliability of remote sensing data. Current spatial data quality check mainly focuses on topological relationship, attribute consistency, and data correlation, ignoring the authenticity of land type interpretation in vector patches. Therefore, this paper proposes a method of checking the authenticity of land type interpretation in vector patches using deep learning and high-resolution remote sensing images, which is designed to meet the urgent need of authenticity check for vector patch categories in major national and industrial projects. In this paper, a method of automatic sample labeling and purification is proposed, which uses the geographic location information of the original map spot to realize processing unit labels automatically and uses two different classifiers combined with cross validation to achieve automatic sample purification, to obtain a large number of high-quality labeled samples for deep learning model training. The classic deep learning model (i.e., Inception_v3) is used for transfer learning to classify segmented images into automatic scene classifications. The overall classification accuracy of the deep learning model in the study area reaches 0.934, the average precision is 0.937, and the average recall is 0.928. Based on the scene classification results using high-resolution remote sensing images, the original vector patches are overlapped and analyzed. By connecting related fields, the segmentation units with inconsistent category information are automatically identified as suspicious patches. We further verified the authenticity check method of land type interpretation of vector patches in Qingshan Quan Town and Dawu Town, Jiawang District, and Xuzhou City. The experimental results show that the precision and recall of our method for these study areas are as high as 0.925 and 0.817, respectively. The method proposed in this paper provides a useful technical support for the check of the authenticity of the land type interpretation of vector patches.

Key words: deep learning, Inception_v3, high resolution remote sensing image, spatial data quality check, vector patch, transfer learning, scene classification, authenticity