CHANGE DETECTION OF BUILDING FOOTPRINTS FROM AIRBORNE LASER SCANNING ACQUIRED IN SHORT TIME INTERVALS
CHANGE DETECTION OF BUILDING FOOTPRINTS FROM AIRBORNE LASER SCANNING ACQUIRED IN SHORT TIME INTERVALS
复制标题
通过机载激光扫描在短时间内获取的建筑足迹变化检测
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
M. Vetter
中科院分区：
文献类型：
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作者：
M. Rutzinger;B. Rüf;B. Höfle;M. Vetter
最近的几项研究表明，城市地区的机载激光扫描（ALS）为3D城市建模和地图更新提供了有价值的信息。从多时相ALS的建筑物足迹检测缺乏可比性，因为改变ALS飞行参数，飞行季节，插值设置，如果使用数字高程模型，以及所使用的建筑物检测方法来处理这些影响的能力。到目前为止，较少关注在短时间跨度内（约10分钟）建筑物的变化检测。该项目的主要问题是植被随时间变化很大，并区分临时物体和目前正在建造和拆除的建筑物的微小变化。我们引入了一个基于对象的工作流程来研究如何定义未改变的对象，允许对象外观的变化来定义对象为未改变的，以及在哪个阈值可以指示变化。试验地点位于因斯布鲁克市（奥地利），可获得2005年夏季和秋季的ALS数据。在初始步骤中，通过基于对象的图像分析（OBIA）检测方法独立地针对每次飞行导出建筑物足迹。建筑物检测的参数推导出一个训练网站，以自动推导出的分类树的规则。然后，从不同的航班衍生的建筑物的对象特征进行比较，并分为类不变的建筑物，新的建筑物，拆除的建筑物，新的建筑物部分，和拆除的建筑物部分。结果通过参考进行验证，该参考是通过目视检查两个时期的高程差图像手动创建的。对于新的建筑物和建筑物部分，90%和拆除的建筑物和建筑物部分，32%被正确检测。被拆除的建筑物的检测强烈影响的外观高的植被，这是由于树木的高度降低，通过比较夏季（叶）和秋季（叶关闭）ALS数据。
Several recent studies have shown that airborne laser scanning (ALS) of urban areas delivers valuable information for 3D city modelling and map updating. Building footprint detection from multi-temporal ALS lacks in comparability because of changing ALS flight parameters, flying season, interpolation settings if digital elevation models are used, and the ability of the used building detection method to deal with these influences. So far, less attention has been paid to change detection of buildings within a short time span (approx. three months), where major problems are the high variability of vegetation over time and to distinguish temporary objects from small changes of buildings, which are currently under construction and demolition, respectively. We introduce an object-based workflow to investigate how unchanged objects can be defined, which variability in the object appearance is allowed to define an object as unchanged, and at which threshold a change can be indicated. The test site is situated in the city of Innsbruck (Austria) where ALS data is available from summer and autumn in 2005. In an initial step building footprints are derived by an object-based image analysis (OBIA) detection method for each flight independently. The parameters for building detection are derived for a training site in order to automatically derive the rules of the classification tree. Then the object features of buildings derived from the different flights are compared to each other and separated into the classes unchanged building, new building, demolished building, new building part, and demolished building part. The results are verified by a reference, which was created manually by visual inspection of the elevation difference image of both epochs. For new buildings and building parts 90% and for demolished buildings and building parts 32% were detected correctly. The detection of demolished buildings is strongly influenced by the appearance of high vegetation, which is caused by the decreasing heights of trees by comparing summer (leaf-on) and autumn (leaf-off) ALS data.