SEMI-AUTOMATIC ROAD NETWORK EXTRACTION FROM DIGITAL IMAGES USING OBJECT-BASED CLASSIFICATION AND MORPHOLOGICAL OPERATORS

Abstract: The demand for geospatial data concerning road network is constant, due to the wide variety of application which needs this type of data. It stands out the importance of this data in cartography update cycles, that can be obtained using automated processes of feature extraction in digital images, which are more accurate, fast and less costly than the traditional methods. In this sense, this work aimed the road network extraction from RapidEye satellite imagery, by developing a hybrid methodology using techniques of object-based image classification and morphological operators. The methodology was tested in three different sites, with images acquired in distinct dates, and the extraction process was evaluated through metrics obtained from the linear matching procedure. By the proposed extraction process, were achieved in terms of correctness and completeness the values of 92.23% and 85.15% for test site 1, the values of 79.16% and 81.06% for test site 2, and the values of 82.05% and 92.22% for test site 3, respectively. The results shown that the proposed methodology presented a good performance for semi-automatic road network extraction from Rapideye images, representing an alternative to auxiliary road network database acquisition and updating.

摘要：鉴于各类应用对路网地理空间数据的广泛需求，此类数据的需求始终保持旺盛。此类数据在地图制图更新周期中的重要性尤为突出，而通过数字影像的自动化特征提取流程即可获取这类数据，相较于传统方法，该方式精度更高、速度更快且成本更低。有鉴于此，本研究旨在通过融合面向对象影像分类（object-based image classification）与形态学算子（morphological operators）技术的混合方法，从快速眼（RapidEye）卫星影像中提取路网信息。该方法在三个不同的试验区域开展了测试，所用影像的获取日期各不相同，并通过线性匹配流程得到的评估指标对提取结果进行了评估。经所提出的提取流程处理后，试验区域1的正确率与完整率分别为92.23%和85.15%，试验区域2为79.16%和81.06%，试验区域3则为82.05%和92.22%。实验结果表明，所提方法在基于快速眼卫星影像的半自动化路网提取任务中表现优异，可作为辅助路网数据库获取与更新的可行替代方案。