材质和倾角属性筛选的光伏屋顶提取

针对城市光伏屋顶类型多样致使样本不平衡，高空间分辨率卫星影像光伏屋顶提取问题，提出了一种材质和倾角属性筛选的方法。通过选取天津市南开区、红桥区、和平区、河东区卫星影像，利用标注于光伏屋顶的材质和倾角两种属性筛选出样本均衡的数据集，借助TensorFlow框架下集成的U-Net算法并合理设置参数，得到了光伏屋顶提取结果，并与未利用材质和倾角两种属性筛选情况下的光伏屋顶提取结果进行对比。试验结果表明：本文方法提取精度较高，尤其是对于研究区域不常见的光伏屋顶，能够提取出更为完整准确且边界清晰的结果。

Photovoltaic roof extraction from material and angle attribute filtered method

Aiming at the high-resolution satellite image photovoltaic roof extraction problem under the background of sample imbalance caused by various types of urban photovoltaic roof,a material and angle attribute filtered method is proposed. By selecting satellite images of Nankai district,Hongqiao district,Heping district,and Hedong district in Tianjin,the sample balanced data set is acquired using the two filtered material and angle attributes labeled on the photovoltaic roof. This paper finally gets the photovoltaic roof extraction result by properly setting parameters of the U-Net algorithm integrated under the Tensor Flow framework. As a comparison,the photovoltaic roof is extracted without using two filtered material and angle attributes at the same time. The experiment shows that the method proposed in this paper has higher extraction accuracy,and especially for the uncommon photovoltaic roof in the study area,it can extract more complete and accurate result with clear boundary.