Accuracy Assessment Measures for Object Extraction from Remote Sensing Images
Accuracy Assessment Measures for Object Extraction from Remote Sensing Images
复制标题
遥感影像目标提取精度评估措施
DOI：
10.3390/rs10020303
复制
发表时间：
2018-02-01
期刊：
影响因子：
5
通讯作者：
Hao, Ming
中科院分区：
文献类型：
--
作者：
Cai, Liping;Shi, Wenzhong;Hao, Ming
遥感影像目标提取具有重要的应用价值，而面向目标的精度评估是保证遥感影像目标提取质量的关键。为了评估对象提取的准确性，本文提出了几种新的准确性措施，不同的规范。首先，基于混淆矩阵给出了基于区域和基于目标个数的精度评估方法。第二，通过结合多个特征的相似性，提供不同的准确性评估措施。第三，为了提高对象提取准确度评估结果的可靠性，设计了两种基于对象细节差异的准确度评估措施。与现有方法相比，该方法综合了特征相似度和距离差异度，大大提高了目标提取评价的可靠性。两个QuickBird图像上令人鼓舞的结果表明，进一步使用所提出的算法的潜力。
Object extraction from remote sensing images is critical for a wide range of applications, and object-oriented accuracy assessment plays a vital role in guaranteeing its quality. To evaluate object extraction accuracy, this paper presents several novel accuracy measures that differ from the norm. First, area-based and object number-based accuracy assessment measures are given based on a confusion matrix. Second, different accuracy assessment measures are provided by combining the similarities of multiple features. Third, to improve the reliability of the object extraction accuracy assessment results, two accuracy assessment measures based on object detail differences are designed. In contrast to existing measures, the presented method synergizes the feature similarity and distance difference, which considerably improves the reliability of object extraction evaluation. Encouraging results on two QuickBird images indicate the potential for further use of the presented algorithm.