由于总体精度或Kappa系数的遥感影像分割/分类评价指标,对影像分割图斑的几何形状等真实结构未能有效刻画,不能有效体现面向对象处理中边缘像元的真实分割/分类效果。本文基于分割对象的几何结构,提出了5个面向对象的高分辨率遥感影像分割/分类精度评价指标:过分割、欠分割、边缘匹配、分割块数,以及形状误差,并在IDL平台实现了一个面向对象影像分析与评价的原型系统。通过对福州市QuickBird影像的Meanshift分割评价,证实了其指标能够刻画出分割对象的深层结构,并符合地物对象分割/分类的真实分布。实验还表明,该评价指标在确定分割算法的参数方面具有重要的应用价值。

Traditional classification accuracy assessments in terms of overall accuracy or Kappa coefficient based on isolating pixels statistic, cannot capture the geometrical properties of the segmented image objects, and therefore do not provide an accurate evaluation in object based segmentation or classification. Considering the importance of bordering pixels and geometric shape in object segmentation, it is thus of our interesting to design some measuring indexes introduced the border pixels and geometric structure information to evaluate the object-based resultant image segmentation or classification. This paper improved five indexes originally proposed by Persello and Bruzzone to evaluate the resultant segmentation of high spatial resolution image. Realizing that the original indices cannot well capture the gap among different categories, resulting in difficulties in discriminating the assessment results of different categories, we use normalized techniques based on geometric shape to overcome it. The indices depend on the geometry features of each object of the thematic map including over segmentation, under segmentation, edge location, fragmentation error and shape error. Moreover, we realized a prototype system contained the aforementioned evaluated indexes based on IDL platform to support the object-based image processing and analysis. To validate these indexes, a subset QuickBird image located on Fuzhou was implemented and the results of the Meanshift segmented algorithm demonstrate that the proposed indexes can provide better accuracy evaluation of each land cover class, and can make users more effectively choose the best classification map. Moreover, our experiments also demonstrate that, compared with OA and Kappa coefficient, the proposed indexes have advantages on characterizing the detailed ground materials and are helpful in aiding the optimal parameters selection for the Meanshift segmented algorithm.