摘要
在复杂城区内部通常存在大量的阴影,建筑物的屋顶也有多种类型,这使得利用高分辨率遥感图像自动提取建筑物变得困难。针对上述2个问题,提出了一种综合利用高分辨率图像与机载Li DAR数据的城市建筑物提取新方法。首先,对归一化植被指数(normalized difference vegetation index,NDVI)和Li DAR高度数据设定阈值得到初步的建筑物提取结果;然后,分别利用阴影区NDVI、图像纹理和形态学滤波来改进结果;最后,采用局部的机载Li DAR数据和Quick Bird图像,对提出的方法进行验证,并与现有方法进行比较。研究结果表明,该方法可有效减少由阴影和不同屋顶特征所造成的错误识别,显著提高了建筑物提取精度。
The occurrence of shadow and diverse building roofs in complex urban areas makes it difficult to extract building automatically using very high resolution（ VHR） imagery over these areas. In order to solve these two problems,this paper proposed a novel method for building extraction using airborne Li DAR data and VHR imagery.The buildings were initially extracted by thresholding the normalized difference vegetation index（ NDVI） image and Li DAR height data. The initially obtained result was then refined by using NDVI image over shadow areas,image texture and morphological filtering. The proposed method was quantitatively evaluated and compared with existing methods using airborne Li DAR data and Quick Bird image of Nanjing City,China. The results indicated that the proposed method effectively reduced the extraction errors caused by shadow and diverse building roof and significantly improved the accuracy of building extraction.
出处
《国土资源遥感》
CSCD
北大核心
2016年第2期106-111,共6页
Remote Sensing for Land & Resources
关键词
建筑物提取
高分辨率图像
机载LIDAR
阴影
冷色屋顶
多层次
building extraction
very high resolution imagery
airborne Li DAR
shadow
cool-colored roof
multi-level
