测绘通报 ›› 2020, Vol. 0 ›› Issue (10): 110-113,122.doi: 10.13474/j.cnki.11-2246.2020.0330

• 技术交流 • 上一篇    下一篇

一种基于联合特征的地表覆盖变化自动检测方法

孔晖, 金洪芳   

  1. 浙江省测绘科学技术研究院, 浙江 杭州 311100
  • 收稿日期:2020-04-02 出版日期:2020-10-25 发布日期:2020-10-29
  • 作者简介:孔晖(1984-),女,工程师,主要从事基础测绘、地理国情监测等方面的应用与研究。E-mail:277314520@qq.com
  • 基金资助:
    浙江省基础公益研究计划(LGF18D010005);浙江省测绘与地理信息局2017年科技课题

An automatic change detection method of surface coverage based on joint features

KONG Hui, JIN Hongfang   

  1. Zhejiang Academy of Surveying and Mapping, Hangzhou 311100, China
  • Received:2020-04-02 Online:2020-10-25 Published:2020-10-29

摘要: 地表覆盖的高效变化检测在地理国情监测中具有重要意义。本文针对当前地表覆盖检测人工目视解译方法效率低,以及软件自动解译错检率、漏检率较高的特点和现状,提出了一种基于联合特征的地表覆盖类型自动变化检测方法。该方法通过对比7种不同的特征联合方案,确立了联合灰度共生矩阵、灰度直方图、光谱统计特征、对象特征的最优组合形式,并设计支持向量机高维度分类器进行分类。试验结果表明,在浙江省复杂地表覆盖分布情况下,基于分辨率优于1 m的国产高分卫星影像,该方法对房屋建筑区、建筑工地等人工构筑物类型变化检测的正确率达到85%以上,对耕地、草地等植被类型也能取得较好的检测效果。

关键词: 地表覆盖, 联合特征, 支持向量机, 自动, 检测

Abstract: Efficient change detection method of land cover has important significance in geographical conditions monitoring. In view of the low efficiency of manual visual interpretation and the high error rate and omission rate of software automatic interpretation in current land cover detection, an automatic change detection method of land cover types based on union feature is proposed in this study. Through the comparison of seven different feature combination schemes, the optimal combination form of combination gray co-occurrence matrix, gray histogram, spectral statistical characteristics and object characteristics is established. Besides, support vector machine high-dimensional classifier is designed for classification. The experiment results show that, in the case of complex land cover distribution in Zhejiang province, the accuracy of this method is more than 85% in detecting the type change of artificial structures such as building areas and construction sites, based on the domestic high-resolution satellite images with resolution better than 1 m. Change detection of grassland and cultivated land can also achieve good results.

Key words: surface coverage, joint features, support vector machine, automatic, detect

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