State Key Lab of Remote Sensing Sciences, Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS), Beijing 100101, China.
Beijing Municipal Institute of City Planning & Design, Beijing 100045, China.
Sensors (Basel). 2019 Mar 7;19(5):1164. doi: 10.3390/s19051164.
This paper presents a novel approach for semantic segmentation of building roofs in dense urban environments with a Deep Convolution Neural Network (DCNN) using Chinese Very High Resolution (VHR) satellite (i.e., GF2) imagery. To provide an operational end-to-end approach for accurately mapping build roofs with feature extraction and image segmentation, a fully convolutional DCNN with both convolutional and deconvolutional layers is designed to perform building roof segmentation. We selected typical cities with dense and diverse urban environments in different metropolitan regions of China as study areas, and sample images were collected over cities. High performance GPU-mounted workstations are employed to perform the model training and optimization. With the building roof samples collected over different cities, the predictive model with convolution layers is developed for building roof segmentation. The validation shows that the overall accuracy (OA) and the mean Intersection Over Union (mIOU) of DCNN-based semantic segmentation results are 94.67% and 0.85, respectively, and the CRF-refined segmentation results achieved OA of 94.69% and mIOU of 0.83. The results suggest that the proposed approach is a promising solution for building roof mapping with VHR images over large areas in dense urban environments with different building patterns. With the operational acquisition of GF2 VHR imagery, it is expected to develop an automated pipeline of operational built-up area monitoring, and the timely update of building roof map could be applied in urban management and assessment of human settlement-related sustainable development goals over large areas.
本文提出了一种新的方法，利用深度卷积神经网络（DCNN）和中国高分辨率（VHR）卫星（即 GF2）图像，对密集城市环境中的建筑屋顶进行语义分割。为了提供一种从特征提取到图像分割的端到端的操作方法，以便准确地对建筑屋顶进行映射，我们设计了一个具有卷积层和反卷积层的全卷积 DCNN 来进行建筑屋顶分割。我们选择了中国不同大都市区具有密集和多样化城市环境的典型城市作为研究区域，并在城市上空采集了样本图像。高性能 GPU 工作站用于执行模型训练和优化。利用在不同城市采集的建筑屋顶样本，开发了基于卷积层的预测模型，用于建筑屋顶分割。验证结果表明，基于 DCNN 的语义分割结果的总体精度（OA）和平均交并比（mIOU）分别为 94.67%和 0.85，而条件随机场（CRF）细化分割结果的 OA 为 94.69%，mIOU 为 0.83。结果表明，该方法是一种很有前途的解决方案，可以利用高分辨率图像对密集城市环境中的大面积建筑物进行映射，且不同的建筑物模式都适用。随着 GF2 VHR 图像的业务化获取，有望开发一个用于业务化建成区监测的自动化流程，并且可以及时更新建筑物屋顶图，以应用于城市管理和评估与人类住区相关的可持续发展目标。
