UAV technology when aided with the unique data acquisition strategies, preprocessing techniques and analytical abilities of an established domain of remote sensing provide more affordable, customized and user-friendly option of “UAV-Remote Sensing”. This extended branch of remote sensing flourishes in both the mapping and measurement, if implemented in the ordered fashion to ensure remote sensing grade data. The current study integrates the potential of UAV technology to the high-resolution data classification approach of object-based image analysis. Department of Civil Engineering, Indian Institute of Technology-Roorkee, India, is selected as study area. In the first part of the study, a detailed UAV survey followed by UAV data processing was carried out to capture the VHR orthorectified image of the selected study area. In the second step, a comparative assessment of nearest neighbor (NN) and rule-based classifications were performed. Orthorectified image was segmented using a multi-resolution segmentation. The overall accuracy for NN and rule-based classifier were 95.13% and 93.87%, respectively. Detailed assessment of user accuracy and producer accuracy described that tree, road, solar panel and waterbody were more accurately classified with NN classifier, whereas building, grass land, open land and vehicle were more accurately classified with rule-based classifier.

中文翻译：

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用于校园监控的无人机遥感：最近邻和基于规则的分类的比较评估

无人机技术与已建立的遥感领域的独特数据采集策略、预处理技术和分析能力相辅相成，提供了更实惠、定制化和用户友好的“无人机-遥感”选项。如果以有序的方式实施以确保遥感等级数据，则遥感的这一扩展分支在制图和测量方面都将蓬勃发展。目前的研究将无人机技术的潜力整合到基于对象的图像分析的高分辨率数据分类方法中。印度理工学院土木工程系被选为研究区。在研究的第一部分，进行了详细的无人机调查，然后进行无人机数据处理，以捕获所选研究区域的 VHR 正射校正图像。第二步，对最近邻 (NN) 和基于规则的分类进行了比较评估。使用多分辨率分割对正射校正图像进行分割。NN 和基于规则的分类器的总体准确率分别为 95.13% 和 93.87%。对用户准确性和生产者准确性的详细评估表明，使用 NN 分类器更准确地分类树木、道路、太阳能电池板和水体，而使用基于规则的分类器更准确地分类建筑物、草地、开阔地和车辆。