Früher|Nächster

Scale estimation of object-oriented image analysis based on spectral-spatial statistics

  • role： First author第一作者
  • Affiliation：

    School of Information Engineering, China University of Geosciences, Beijing 100083, China

  • Email：mayanni@cugb.edu.cn
  • Introduction：马燕妮(1992—　)，女，硕士研究生，研究方向为高空间分辨率遥感影像信息提取。E-mail：mayanni@cugb.edu.cn
MA Yanni1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Information Engineering, China University of Geosciences, Beijing 100083, China

  • Email：mingdp@cugb.edu.cn
  • Introduction：明冬萍(1976—　)，女，副教授，研究方向为遥感信息提取及地学尺度。E-mail：mingdp@cugb.edu.cn
MING Dongping1*,  
  • Affiliation：

    School of Computer Science, Zhejiang University of Technology, Hangzhou 310014, China

YANG Haiping2

Resümee

Multi-scale segmentation is the key step of analysis in remotely-sensed imagery. Scale parameter selection in the segmentation process is directly related to the quality and accuracy of object-oriented analysis. Only on the basis of experience for segmentation parameter choice that has less quantitative analysis ways, currently. These methods lack quantitative estimation before segmentation, with considerable workloads and low efficiency. From the perspective of scientific research, the scientificity and universality of these methods are poor. The objective of this paper is to use the quantitative method to determine the scale parameterin order to realize the automatic extracting target on the object-oriented analysis. This paper summarizes the concept of scale parameter in the object-oriented analysis. The commonly used segmentation scale parameters into spatial and attribute bandwidths were also analyzed. This paper used a spatial and spectral statistics-based scale parameter selection method for object-based information extraction from high spatial resolution remote sensing images. The relationship between Fractal Net Evolution Approach (FNEA) in multi-scale segmentation and spatial statistical characteristics was analyzed. Scale estimation based on spatial and spectral statistical characteristics was applied to FNEA in multi-scale segmentation. Meanwhile, the scale estimation approach proposed in this paper was verified by high spatial resolution image, namely IKONOS and SPOT 5 data. Construction and farmland areas were selected for spatial and spectral statistical characteristics, respectively, to further estimate the optimal scale parameters in segmentation. A series of supervised classification was performed to verify the reasonability of the predicted optimal scale parameter. The classification and accuracy assessment results show that the estimated scale by spatial statistical characteristics is basically close to the optimal one in the FNEA-based multi-scale segmentation. The proposed scale estimation approach can ensure the accuracy of the following object-oriented image classification. The method can be used to estimate the appropriate scale parameters before segmentation. In addition, it is an essentially data-driven method that requires nearly no prior knowledge; thus, it can enhance the efficiency and automatic degree of object-based image analysis.

Schlüsselwort

object oriented image analysis;image segmentation;scale estimation;scale selection;spatial statistics;fractal net evolution approach

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