Abstract: The key parameters of houses such as distribution, area and height play an important role for urban-rural planning, earthquake emergency and disaster mitigation. The computer automatic extraction method is an effective way to acquire large area house information using satellite-borne or airborne optical remote-sensing images. However, because of the similarity of spectral characters for different land cover types or the influence of snow coverage, the classification accuracy of house type using traditional spectral based method can be decreased. To acquire the accurate houses distribution, a method based on the height information is proposed using unmanned aerial vehicle(UAV)in this study. With UAV flying at the height of 100m above ground, the route of the UVA was planned with the heading direction overlap of 77% and side direction overlap of 50%for the nearby pictures. Taking Qionghalajun Village in Xinjiang Uygur Autonomous Region for example, 69 pictures of the study area were obtained with DJI Phantom 3 professional. With those pictures input into the EasyUAV software, the Digital Elevation Model(DEM), Digital Surface Model(DSM), and Digital Orthophoto Map(DOM)were acquired based on photogrammetry method using the overlapped optical remote-sensing images of UAV. After that, the house distribution and height were acquired with the differences between DSM and DEM images larger than 2.6m. To eliminate the influences of disintegrated pixels on the house extraction, mainly caused by the trees or noise point, the classification aggregation tool of ENVI software was used with the disintegrated pixels' area less than 4m². Compared with visual interpretation result, the user accuracy and mapping accuracy of the house extraction method proposed in this study is 88.69% and 97.42%, respectively. In addition, to evaluate the performance of the proposed method, the result of traditional supervised classification method using DOM data acquired previously was compared with the result of new method. The results show that the new method is more accurate the user accuracy and mapping accuracy of the supervised classification method, which is 43.23% and 85.30%, respectively. Besides the study area in this study, the performance of the proposed method will be evaluated at the other places in the further study.

Key words: house distribution, optical remote-sensing images, unmanned aerial vehicle, photogrammetry, supervised classification

摘要： 房屋的分布位置、面积和高度等信息对城乡规划、地震应急和减灾等方面具有非常重要的作用。利用星载或机载可见光遥感图像进行地物信息自动提取，是高效快速获取大面积房屋数据的重要手段。但是，当出现同物异谱或异物同谱现象，以及地物遮挡的影响时，基于光谱信息的传统地物分类或提取方法房屋提取精度较低。文中选取新疆维吾尔自治区琼哈拉峻村房屋和农田光谱信息相似的地区，利用小型旋翼无人机获取了在航向和旁向具有一定重叠度的可见光遥感图像。然后，基于摄影测量原理获得了研究区的数字表面模型（Digital Surface Model，DSM）、数字高程模型（Digital Elevation Model，DEM）和数字正射影像数据，并在此基础上通过阈值分析DSM和DEM差值提取出房屋像元及其面积和高度等信息。通过与传统监督分类法对比发现，使用摄影测量方法基于高度信息的房屋提取其用户精度和制图精度分别为88.69%和97.42%，而监督分类法提取房屋的精度分别为43.23%和85.30%，说明文中提出的方法在房屋和背景信号差别较小的区域时精度更高。

关键词: 房屋分布, 可见光遥感, 无人机, 摄影测量, 监督分类