Optimizing multi-resolution segmentation scale using empirical methods: Exploring the sensitivity of the supervised discrepancy measure Euclidean distance 2 (ED2)
Optimizing multi-resolution segmentation scale using empirical methods: Exploring the sensitivity of the supervised discrepancy measure Euclidean distance 2 (ED2)
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DOI：
10.1016/j.isprsjprs.2013.11.006
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发表时间：
2014-01-01
影响因子：
12.7
通讯作者：
Civco, Daniel L.
中科院分区：
文献类型：
--
作者：
Witharana, Chandi;Civco, Daniel L.
多分辨率分割（MRS）已被证明是基于地理对象的图像分析（GEOBIA）框架中最成功的图像分割算法之一。该算法相对复杂且依赖于用户；尺度、形状和紧凑性是用户可用于控制算法的主要参数。多个分割结果是常见的，因为每个参数可以取其参数空间内的一系列值或者参数之间的值的不同组合。通过试错过程寻找最佳参数值通常会耗费时间和劳动力，因此，已经提出并测试了几种用于监督自动参数设置的替代监督和无监督方法。在监督经验评估的情况下，采用差异度量来计算参考多边形和图像对象候选之间的相异性度量。显然，最佳参数预测的可靠性在很大程度上依赖于分割质量度量的敏感性。追求最佳参数设置背后的想法是，例如，给定的比例设置提供与其他比例设置不同的图像对象候选；因此，通过设计，监督质量指标应该捕获这种差异。在这项探索性研究中，我们选择欧几里德距离2（ED2）度量，这是最近提出的监督度量，其主要设计目标是优化二维欧几里德空间中图像对象与参考多边形之间的几何差异（潜在分割误差（PSE））和算术差异（分割数量比（NSR）），作为研究有效性和功效的候选度量。 用于寻找 MRS 算法的最佳尺度参数设置的经验差异测量。我们选择了来自四个不同空间分辨率和场景内容的不同星载传感器的测试图像场景，并在一系列参数设置下使用 MRS 算法对它们进行系统分割。使用非参数统计方法测试了不同尺度组的 ED2 指标的判别能力。我们的结果表明，ED2 度量在较小尺度值下显着区分图像对象候选的质量，但在较大尺度值下失去了敏感性。这就质疑了 ED2 指标在 MRS 算法参数优化中的意义。我们的论点是，ED2 指标以牺牲时间为代价提供了一些最佳尺度参数的概念。在这方面，特别是在操作级图像处理中，值得重新考虑处理器密集型 MRS 算法在针对不太敏感的质量指标的一系列参数设置下的执行时间与专家主导的试错方法之间的权衡。 (C) 2013 年国际摄影测量与遥感协会 (ISPRS) 由 Elsevier B.V 出版。保留所有权利。
Multiresolution segmentation (MRS) has proven to be one of the most successful image segmentation algorithms in the geographic object-based image analysis (GEOBIA) framework. This algorithm is relatively complex and user-dependent; scale, shape, and compactness are the main parameters available to users for controlling the algorithm. Plurality of segmentation results is common because each parameter may take a range of values within its parameter space or different combinations of values among parameters. Finding optimal parameter values through a trial-and-error process is commonly practiced at the expense of time and labor, thus, several alternative supervised and unsupervised methods for supervised automatic parameter setting have been proposed and tested. In the case of supervised empirical assessments, discrepancy measures are employed for computing measures of dissimilarity between a reference polygon and an image object candidate. Evidently the reliability of the optimal-parameter prediction heavily relies on the sensitivity of the segmentation quality metric. The idea behind pursuing optimal parameter setting is that, for instance, a given scale setting provides image object candidates different from the other scale setting; thus, by design the supervised quality metric should capture this difference. In this exploratory study, we selected the Euclidean distance 2 (ED2) metric, a recently proposed supervised metric, whose main design goal is to optimize the geometrical discrepancy (potential segmentation error (PSE)) and arithmetic discrepancy between image objects and reference polygons (number-of segmentation ratio (NSR)) in two dimensional Euclidean space, as a candidate to investigate the validity and efficacy of empirical discrepancy measures for finding the optimal scale parameter setting of the MRS algorithm. We chose test image scenes from four different space-borne sensors with varying spatial resolutions and scene contents and systematically segmented them using the MRS algorithm at a series of parameter settings. The discriminative capacity of the ED2 metric across different scales groups was tested using non-parametric statistical methods. Our results showed that the ED2 metric significantly discriminates the quality of image object candidates at smaller scale values but it loses the sensitivity at larger scale values. This questions the meaningfulness of the ED2 metric in the MRS algorithm's parameter optimization. Our contention is that the ED2 metric provides some notion of the optimal scale parameter at the expense of time. In this respect, especially in operational-level image processing, it is worth to re-think the trade-off between execution time of the processor-intensive MRS algorithm at series of parameter settings targeting a less-sensitive quality metric and an expert-lead trial-and-error approach. (C) 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.