地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (5): 622-631.doi: 10.3724/SP.J.1047.2016.00622

• 遥感大数据协同计算方法 • 上一篇    下一篇

基于谱空间统计特征的高分辨率影像分割尺度估计

明冬萍1(), 周文1, 汪闽2   

  1. 1. 中国地质大学(北京)信息工程学院,北京 100083
    2. 南京师范大学 虚拟地理环境教育部重点实验室,南京 210023
  • 收稿日期:2015-12-15 修回日期:2016-01-23 出版日期:2016-05-10 发布日期:2016-05-10
  • 作者简介:

    作者简介:明冬萍(1976-),女,博士,副教授,主要从事遥感信息提取及地学尺度研究。E-mail:mingdp@cugb.edu.cn

  • 基金资助:
    国家自然科学基金项目(41371347);中央高校基本科研业务费专项资金项目;国家高分辨率对地观测系统重大专项(03-Y30B06-9001-13/15-01);江苏省自然科学基金项目(BK20140042)

Scale Parameter Estimation Based on the Spatial and Spectral Statistics in High Spatial Resolution Image Segmentation

MING Dongping1,*(), ZHOU Wen1, WANG Min2   

  1. 1. School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China
    2. Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing, Jiangsu 210023, China;;
  • Received:2015-12-15 Revised:2016-01-23 Online:2016-05-10 Published:2016-05-10
  • Contact: MING Dongping E-mail:mingdp@cugb.edu.cn

摘要:

多尺度分割是面向对象遥感影像分析的关键性基础步骤,影像分割过程中尺度参数的选择直接关系到面向对象影像分析的质量和精度。本文首先从理论层面将遥感影像分割的尺度界定为基于统计的原始影像全局或局部特征的一种定量化估计,并在算法层面上将多尺度分割算法的尺度参数概括为空间尺度分割参数(类别或斑块间的空间距离)、属性尺度分割参数(类别或斑块间的属性距离)和合并阈值参数(斑块大小或斑块像元数目);接着,提出了基于谱空间统计的高分辨率影像分割尺度估计方法;最后,以均值漂移多尺度分割算法为例,采用高空间分辨率的Ikonos、Quickbird和航空影像数据,对本文提出的基于谱空间统计的高分辨率影像分割尺度估计方法进行了验证。结果表明,该方法在一定程度上不仅避免了高分辨率遥感影像分割尺度参数选择的主观性和盲目性,还提高了面向对象影像分析的自动化程度,具有可行性和有效性。

关键词: 面向对象影像分析, 影像分割, 尺度估计, 空间统计, 光谱统计

Abstract:

Object-Based Image Analysis (OBIA) is becoming an important technology for the information extraction from high spatial resolution images. Multi-scale image segmentation is a key and fundamental procedure of OBIA, however, the scale selection within the multi-scale image segmentation is always difficult to achieve for the high-performance OBIA. This paper firstly generalizes the commonly used segmentation scale parameters into three aspects: the spatial parameter (the spatial distance between classes), the attribute parameter (the attribute distance or spectral difference between classes) and the merging threshold (the area or pixel number of the minimum useful object). Next, this paper proposes a spatial and spectral statistics-based scale parameter estimation method for OBIA. The main concept of this proposed method is to use the average local variogram (without considering the anisotropism of spatial distribution) or the semivariogram (considering the anisotropism of spatial distribution) to pre-estimate the optimal spatial parameter. Next, the selection of the optimal attribute parameter and the selection of the merging threshold are achieved based on the local variance histogram and the simple geometric computation, respectively. Taking the mean-shift segmentation as an example, this study uses Ikonos, Quickbird and aerial panchromatic images as the experimental data to verify the validity of the proposed scale parameter estimation method. Experiments based on the quantitative multi-scale segmentation evaluation could testify the validity of this method. This pre-estimation based scale parameter selection method is practically helpful and efficient in OBIA. The idea of this method can be further extended to be integrated into other segmentation algorithms and be adaptive to other sensor data.

Key words: object-based image analysis, image segmentation, scale estimation, spatial statistics, spectral statistics