高空间分辨率遥感影像分割定量实验评价方法综述

doi:10.3724/SP.J.1047.2017.00818

摘要/Abstract

摘要：

GEOBIA（Geographic Object-Based Image Analysis）技术针对高空间分辨率遥感影像分析的效果和精度远优于基于像元的传统方法。影像分割作为GEOBIA中的关键技术,学者们对此已经做了大量的研究,提出众多分割算法。对分割算法进行评价和分割技术本身同样重要,通过分割评价可以对分割算法的性能进行评价,比较不同分割算法的优劣,为影像选择合适的分割算法并设定合适的分割参数。影像分割的目的是为了实现影像分析操作的自动化,而主观评价法、系统评价法和分析评价法,因其无法给出客观定量指标的特点,难以应用于实时、自动化的高分辨率影像信息提取与分析系统当中。加之近年来针对分割评价方法的研究远远落后于分割算法本身,因此对定量分割评价方法进行综述对于影像分割方法及其应用研究意义重大。本文对现有的评价方法进行系统总结,建立了针对高空间分辨率遥感影像分割评价方法的分类体系。对各种方法,特别是定量的实验评价法进行对比,分析其应用范围和优劣,最后指出了高空间分辨率遥感影像分割评价未来的改进方向和应用前景。

关键词: 高空间分辨率遥感影像, GEOBIA, 分割质量评价, 差异评价, 优度评价

Abstract:

Geographic Object-Based Image Analysis (GEOBIA) is much better than traditional pixel-based method of high spatial resolution remote sensing image analysis. Since image segmentation is the key technique in GEOBIA, scholars and researchers have already conducted extensive research and proposed a number of segmentation algorithms. In order to compare different segmentation methods and evaluate its own performance, segmentation results need to be evaluated. Therefore, the study of segmentation evaluation is equally important to segmentation algorithm. We could choose the applicable segmentation method and set appropriate parameters for specific images and applied the segmentation evaluation. The aim of image segmentation is to enable the automation of image analysis. However, the evaluation methods which cannot provide quantitative indexes are not applicable in automatic real-time image analysis system. Moreover, research in segmentation evaluation is less than segmentation itself. Thus, it will be significant to study segmentation and review the quantitative evaluation method. In this paper, based on summarizing the evaluation methods, the hierarchy of segmentation evaluation method is presented. In spite of describing quantitative empirical methods, we discussed their range of application. Their advantages and shortcomings were also analyzed. Finally, possible future direction and potential application prospect for high spatial remote sensing image segmentation evaluation were proposed.

Key words: high spatial resolution remote sensing images, OBIA, segmentation evaluation, empirical discrepancy method, empirical goodness method

陈扬洋, 明冬萍, 徐录, 赵璐. 高空间分辨率遥感影像分割定量实验评价方法综述[J]. 地球信息科学学报, 2017, 19(6): 818-830.DOI:10.3724/SP.J.1047.2017.00818

CHEN Yangyang,MING Dongping,XU Lu,ZHAO Lu. An Overview of Quantitative Experimental Methods for Segmentation Evaluation of High Spatial Remote Sensing Images[J]. Journal of Geo-information Science, 2017, 19(6): 818-830.DOI:10.3724/SP.J.1047.2017.00818

图/表 5

图1

图2

图3

图4

表1

典型监督评价混合指标

指标	公式	组合	文献
ED1	$ED 1 = O S 2 + U S 2 2$	欠分割和过分割指标	Clinton^[48]
ED2	$ED 2 = PS E 2 + NS R 2$	欠分割和数量指标	Liu^[37]
ED3	$ED 3 = ∑ ∑ 1 - area r i ? s j area (r i) 2 + 1 - area r i ? s j area (s j) 2 2 ˉ$	局部欠分割和过分割指标	Yang^[49]
SEI	$SE I lo cal i = 1 - area r i ? s j area r i 2 + 1 - area r i ? s j area s j 2 2, s j ∈ S i * 2 1, s j ? S i * 2$ $SEI = 1 n ∑ i = 1 n SE I lo cal i$	基于双向匹配的局部欠分割和过分割指标	Yang^[38]
M	$M = 1 - R A su b ij 2 + 1 - R A supe r ij 2 + R A su b ij 2 + R P ij 2 4$	区域和距离指标	Moller^[46]
ADI	$ADI = O E 2 + C E 2$	欠分割和过分割指标	Cheng J^[50]

表1

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