计算机科学
模板
分割
人工智能
匹配(统计)
模板匹配
模式识别(心理学)
样品(材料)
计算机视觉
航空影像
直线(几何图形)
图像分割
特征提取
可视化
图像(数学)
数学
统计
色谱法
化学
程序设计语言
几何学
作者
Min Wang,Qi Cui,Yujie Sun,Qiao Wang
出处
期刊:Isprs Journal of Photogrammetry and Remote Sensing
日期:2018-04-30
卷期号:141: 100-111
被引量:57
标识
DOI:10.1016/j.isprsjprs.2018.04.010
摘要
In object-based image analysis (OBIA), object classification performance is jointly determined by image segmentation, sample or rule setting, and classifiers. Typically, as a crucial step to obtain object primitives, image segmentation quality significantly influences subsequent feature extraction and analyses. By contrast, template matching extracts specific objects from images and prevents shape defects caused by image segmentation. However, creating or editing templates is tedious and sometimes results in incomplete or inaccurate templates. In this study, we combine OBIA and template matching techniques to address these problems and aim for accurate photovoltaic panel (PVP) extraction from very high-resolution (VHR) aerial imagery. The proposed method is based on the previously proposed region–line primitive association framework, in which complementary information between region (segment) and line (straight line) primitives is utilized to achieve a more powerful performance than routine OBIA. Several novel concepts, including the mutual fitting ratio and best-fitting template based on region–line primitive association analyses, are proposed. Automatic template generation and matching method for PVP extraction from VHR imagery are designed for concept and model validation. Results show that the proposed method can successfully extract PVPs without any user-specified matching template or training sample. High user independency and accuracy are the main characteristics of the proposed method in comparison with routine OBIA and template matching techniques.
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