Segmentation of Shadowed Buildings in Dense Urban Areas from Aerial Photographs
Segmentation of Shadowed Buildings in Dense Urban Areas from Aerial Photographs
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DOI：
10.3390/rs4040911
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发表时间：
2012-03
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影响因子：
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通讯作者：
J. Susaki
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文献类型：
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作者：
J. Susaki
利用遥感图像对城市地区，特别是人口密集的城市地区的建筑物进行分割是非常可取的。然而，通过使用现有的算法得到的分割结果是不令人满意的，因为不清楚的边界之间的建筑物和阴影的相邻建筑物。在本文中，提出了一种算法，成功地分割建筑物从航空照片，包括阴影建筑物在密集的城市地区。为了处理具有粗糙纹理的屋顶，将数字（DN）量化为若干量子值。在分割期间应用使用几个间隔宽度的量化，并且对于每个量化，在图像中标记具有均匀值的区域。然后合并从在每次量化时获得的均匀区域确定的边缘，并提取经常观察到的边缘。通过使用“矩形索引”，形状接近矩形的区域因此被选择为建筑物。实验结果表明，该算法比现有的分割算法具有更好的分割效果。因此，成功分割阴影屋顶的主要因素是（1）不同量化结果的组合，（2）根据矩形索引选择建筑物，以及（3）通过包含具有高概率为边缘的非边缘像素来完成边缘。利用这些因素，该算法优化的空间滤波尺度相对于在一个地方的建筑物屋顶的大小。所提出的算法被认为是有用的进行建筑物分割为各种目的。
Segmentation of buildings in urban areas, especially dense urban areas, by using remotely sensed images is highly desirable. However, segmentation results obtained by using existing algorithms are unsatisfactory because of the unclear boundaries between buildings and the shadows cast by neighboring buildings. In this paper, an algorithm is proposed that successfully segments buildings from aerial photographs, including shadowed buildings in dense urban areas. To handle roofs having rough textures, digital numbers (DNs) are quantized into several quantum values. Quantization using several interval widths is applied during segmentation, and for each quantization, areas with homogeneous values are labeled in an image. Edges determined from the homogeneous areas obtained at each quantization are then merged, and frequently observed edges are extracted. By using a “rectangular index”, regions whose shapes are close to being rectangular are thus selected as buildings. Experimental results show that the proposed algorithm generates more practical segmentation results than an existing algorithm does. Therefore, the main factors in successful segmentation of shadowed roofs are (1) combination of different quantization results, (2) selection of buildings according to the rectangular index, and (3) edge completion by the inclusion of non-edge pixels that have a high probability of being edges. By utilizing these factors, the proposed algorithm optimizes the spatial filtering scale with respect to the size of building roofs in a locality. The proposed algorithm is considered to be useful for conducting building segmentation for various purposes.