Automatic road extraction from LIDAR data based on classifier fusion

F. Samadzadegan, M. Hahn, B. Bigdeli
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引用次数: 46

Abstract

The ultimate goal of pattern recognition systems in remote sensing is to achieve the best possible classification performance for recognition of different objects such as buildings, roads and trees. From a scientific perspective, the extraction of roads in complex environments is one of the challenging issues in photogrammetry and computer vision, since many tasks related to automatic scene interpretation are involved. Roads have homogeneous reflectivity in LIDAR intensity and the same height as bare surface in elevation. Proposed method in this paper is based on combining multiple classifiers (MCS) is one of the most important topics in pattern recognition to achieve higher accuracy. Majority Voting and Selective Naïve Bays are two methods that used for fusion of classifiers.
基于分类器融合的激光雷达数据道路自动提取
模式识别系统在遥感中的最终目标是在识别建筑物、道路和树木等不同物体时达到最佳的分类性能。从科学的角度来看,复杂环境中的道路提取是摄影测量学和计算机视觉中的一个具有挑战性的问题,因为涉及到许多与自动场景解释相关的任务。道路在激光雷达强度上具有均匀的反射率,在高程上与裸地高度相同。本文提出的基于多分类器组合的方法是模式识别中实现更高准确率的重要课题之一。多数投票和选择性Naïve海湾是用于融合分类器的两种方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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