地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (5): 632-638.doi: 10.3724/SP.J.1047.2016.00632

• 遥感大数据协同计算方法 • 上一篇    下一篇

综合多层优选尺度的高分辨率影像分割

杨海平1,2(), 明冬萍3   

  1. 1. 浙江工业大学计算机学院,杭州 310023
    2. 浙江省海洋大数据挖掘与应用重点实验室,舟山 316022
    3. 中国地质大学(北京)信息工程学院,北京 100083
  • 收稿日期:2016-02-15 修回日期:2016-04-18 出版日期:2016-05-10 发布日期:2016-05-10
  • 作者简介:

    作者简介:杨海平(1987-),女,博士,研究方向为高分辨率遥感影像信息提取。E-mail:yanghp@zjut.edu.cn

  • 基金资助:
    国家自然科学基金项目(41271367、41501453、41371347);浙江省海洋大数据挖掘与应用重点实验室开放课题项目(OBDMA201512);国家高分辨率对地观测系统重大专项(03-Y30B06-9001-13/15-01)

Optimal Scales Based Segmentation of High Spatial Resolution Remote Sensing Data

YANG Haiping1,2,*(), MING Dongping3   

  1. 1. College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou 310023, China
    2. Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province, Zhoushan 316022, China
    3. School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China;
  • Received:2016-02-15 Revised:2016-04-18 Online:2016-05-10 Published:2016-05-10
  • Contact: YANG Haiping E-mail:yanghp@zjut.edu.cn

摘要:

采用面向对象方法处理高空间分辨率遥感影像时,影像分割质量对后续影像的信息提取结果影响很大。本文主要针对高分辨率影像分割中地物多尺度的问题,提出了一种基于多层优选尺度的高分辨率影像分割算法。该算法首先采用一系列规律变化的尺度对高分辨率影像进行多尺度分割,然后通过单分割层全局标准差的变化与尺度的关系确定一组最优分割尺度。在此基础上,通过各优选分割层之间的包含关系,局部建立多层次对象树,从整体上形成影像森林;通过局部同质性异质性综合评价指数的比较及父层光谱特征的限制来选取多层次对象树中的优势对象,从而获得最终的高分辨率影像分割结果。最后,本文分别采用了Geoeye和ZY3多光谱影像进行了2组分割实验,结果表明本文算法能有效地提高正常分割影像对象的比例。

关键词: 高分辨率影像, 分割, 多尺度, 多层次对象树, 综合评价指数

Abstract:

The quality of image segmentation has a great impact on the results of information extraction from high spatial resolution remote sensing imagery when the object-based method is employed. During the segmentation of high spatial resolution remote sensing images, the scale parameter directly affects the construction of segmented image objects. A small scale is likely to produce broken image objects, while a large scale probably results in the mixed image objects. To solve this problem, an image segmentation framework based on a set of optimal scales is proposed in this paper. First of all, the high spatial resolution remote sensing image is processed using multi-scale segmentation methods with respect to a group of regularly distributed scales. Then the relationship between the global standard deviation of a single segmented layer and its corresponding scale is determined, from which a group of optimal scales are selected. Since the object in a layer that is segmented by a big scale parameter contains the corresponding object in a layer that is segmented by a small scale parameter, a hierarchical tree with nodes of multi-scale image objects can be created. Within this hierarchical tree, the image object of the layer that is segmented by the maximum scale is set as the root. In this manner, each image object of the layer that is segmented by the maximum scale can generate a hierarchical tree, which all together forms the image forest. Two types of features are considered when the optimal image object is selected from each hierarchical tree, which are the comprehensive evaluation index and the spectral features. The comprehensive evaluation index keeps a balance between the homogeneity and heterogeneity of the image objects. And the spectral features of the children nodes should be consistent with the parent nodes in order to dismiss the mixed image objects. Finally, the segmented result is created after the optimal image objects from all hierarchical trees are selected. In the experiment presented in this paper, the Geoeye and ZY3 images are adopted. Results show that the proposed method can effectively improve the percentage of properly segmented image objects.

Key words: high spatial resolution remote sensing imagery, segmentation, multiple scales, a hierarchical tree with nodes of multi-scale image objects, comprehensive evaluation index