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A Novel Protocol for Accuracy Assessment in Classification of Very High Resolution Images

机译：高分辨率图像分类中精度评估的新协议

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摘要

This paper presents a novel protocol for the accuracy assessment of the thematic maps obtained by the classification of very high resolution images. As the thematic accuracy alone is not sufficient to adequately characterize the geometrical properties of high-resolution classification maps, we propose a protocol that is based on the analysis of two families of indices: 1) the traditional thematic accuracy indices and 2) a set of novel geometric indices that model different geometric properties of the objects recognized in the map. In this context, we present a set of indices that characterize five different types of geometric errors in the classification map: 1) oversegmentation; 2) undersegmentation; 3) edge location; 4) shape distortion; and 5) fragmentation. Moreover, we propose a new approach for tuning the free parameters of supervised classifiers on the basis of a multiobjective criterion function that aims at selecting the parameter values that result in the classification map that jointly optimize thematic and geometric error indices. Experimental results obtained on QuickBird images show the effectiveness of the proposed protocol in selecting classification maps characterized by a better tradeoff between thematic and geometric accuracies than standard procedures based only on thematic accuracy measures. In addition, results obtained with support vector machine classifiers confirm the effectiveness of the proposed multiobjective technique for the selection of free-parameter values for the classification algorithm.
机译：本文提出了一种新的协议，用于通过对超高分辨率图像的分类获得的主题图的准确性评估。由于仅主题准确度不足以充分表征高分辨率分类图的几何特性，因此我们提出了一种协议，该协议基于对两个索引系列的分析：1）传统主题准确性索引和2）一组新颖的几何索引，可对地图中识别的对象的不同几何属性进行建模。在这种情况下，我们提出了一组索引，这些索引描述了分类图中五种不同类型的几何误差：1）过度分割； 2）细分市场； 3）边缘位置； 4）形状失真； 5）碎片化。此外，我们提出了一种基于多目标准则函数来调整监督分类器的自由参数的新方法，该目标函数的目的是选择导致分类图共同优化主题和几何误差指标的参数值。在QuickBird图像上获得的实验结果表明，所提出的协议在选择分类图上的有效性，该分类图的特征是，在主题精度和几何精度之间的权衡要比仅基于主题精度度量的标准程序更好。此外，用支持向量机分类器获得的结果证实了所提出的多目标技术对于选择自由参数值进行分类算法的有效性。

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