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遥感技术与应用  2018, Vol. 33 Issue (2): 321-330    DOI: 10.11873/j.issn.1004-0323.2018.2.0321
数据与图像处理     
尺度自适应的高分辨率遥感影像分水岭分割方法
闫鹏飞,明冬萍
Yan Pengfei, Ming Dongping
Segmentation of High Spatial Resolution Remotely Sensed Data Using Watershed with Self-adaptive Parameterization
(中国地质大学(北京)信息工程学院,北京  100083)
(School of Information Engineering, China University of Geosciences (Beijing),Beijing 100083,China)
 全文: PDF(10379 KB)  
摘要:
高空间分辨率遥感影像的分割是面向对象分析的重要基础,大部分影像分割算法都涉及分割参数设置的问题,影像分割方法的参数自适应是影响遥感影像分割效率和效果的关键问题之一。针对传统分水岭分割算法易受噪声干扰且分割尺度参数难以自适应选择的问题,提出了一种尺度自适应的高分辨遥感影像分水岭分割方法,即在对原始影像进行中值滤波的基础上,综合考虑高分遥感影像多个波段信息,并利用区域合并进行分水岭分割,然后在空间统计学思想下,统计不同窗口下局部方差的变程实现分水岭分割参数的自适应设置,进而对高空间分辨率遥感影像进行分割。最后,以IKONOS和QuickBird高空间分辨率多光谱遥感影像作为实验数据,对提出参数自适应的分水岭分割方法的有效性进行了验证。基于分割后斑块内均质性和斑块间异质性指标构建综合评价模型,对本文提出的分割方法所得到的分割结果与不同参数序列的分割结果进行了定量比较,对比结果表明采用该分割算法能够得到较好的分割效果。因此,该方法不仅能一定程度上提高影像分割的精度,也保证了分割参数选择的自动化程度,可为今后的影像分割及参数化研究提供一种思路。

 
关键词: 自适应参数化分水岭分割面向对象分析空间统计
    
Abstract: Segmentation of high spatial resolution remotely sensed image is the important foundation of Object\|Based Image Analysis(OBIA), most of the image segmentation algorithms involve the problem of parameter setting. Self\|adaptive Parameterization is one of the key factors that affect the efficiency and effectiveness of remote sensing image segmentation. Considering that traditional watershed segmentation algorithm is susceptible to noise and the segmentation scale parameter is difficult to be self\|adaptively chosen,this paper propose a scale self\|adaptive method in watershed segmentation. After median filtering in primary image, this paper uses spatial statistical method to realize the self\|adaptive setting of watershed segmentation parameters, and then segments the high spatial resolution remote sensing image. This study uses IKONOS and Quickbird multispectral images as experimental data to testify the validity of the method proposed by this paper. The homogeneity within the segmentation parcels and the heterogeneity between thesegmentation parcels are used to build up a synthetic evaluation model to quantitatively evaluate the segmentation results by the proposed method by comparing with different parameter sequences segmentation results. The comparison result show that the proposed method perform well in high spatial resolution image segmentation. As result, the method proposed in this paper not only improves the accuracy of image segmentation to a certain extent, but also raises the automation of the segmentation parameter selection, which provides a new way for image segmentation and the research of parameterization in the future.
Key words: Self-adaptive parameterization    Watershed segmentation    Object-based image analysis    Spatial statistics
收稿日期: 2017-04-07 出版日期: 2018-05-15
:  TP 79  
基金资助: 国家自然科学基金项目(41371347),国家自然科学基金项目(41671369)。
作者简介: 闫鹏飞(1992-),男,河南修武人,硕士研究生,主要从事高空间分辨率遥感影像信息提取研究。Email:yanpengfei719@163.com。
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引用本文:

闫鹏飞,明冬萍. 尺度自适应的高分辨率遥感影像分水岭分割方法[J]. 遥感技术与应用, 2018, 33(2): 321-330.

Yan Pengfei, Ming Dongping. Segmentation of High Spatial Resolution Remotely Sensed Data Using Watershed with Self-adaptive Parameterization. Remote Sensing Technology and Application, 2018, 33(2): 321-330.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2018.2.0321        http://www.rsta.ac.cn/CN/Y2018/V33/I2/321

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