基于全卷积神经网络的建筑物屋顶自动提取

高分辨率遥感影像在地面自动目标提取中得到了广泛应用,然而利用传统算法,很难高精度地进行实时的建筑物屋顶绘图。本文使用深度学习方法探讨建筑物屋顶分割,由于卷积运算对形变、旋转、光照条件的不敏感,设计了一种用于建筑物屋顶提取的深度卷积神经网络,提出的网络为级联式全卷积神经网络,在深度卷积神经网络的设计中使用了特征复用和特征增强,实现建筑物的自动精确提取。以美国马萨诸塞州建筑物数据集为基础的实验结果表明,本文提出的网络结构取得了92.3%的总体预测精度,和其他方法相比,本文提出的方法具有更高的精度

Automatic Building Roof Extraction with Fully Convolutional Neural Network

The very high resolution remotely sensed imagery has been widely applied in automatic extraction of ground objects, However, it is hard to conduct timely operational building roof mapping with high accuracy using conventional algorithms. This paper investigate building roof segmentation with deep learning method, since the convolution operations upon images is not sensitive to deformation,rotation and illumination condition, a Deep Convolutional Neural Network (DCNN) is designed for building roof extraction, the proposed network has a cascaded structure with fully convolutional layers, with strategies for feature reuse and enhancement in the design of DCNN, it is expected to accurately extract building roof. The experiment is carried out upon building sample data set acquired in Massachusetts, USA, the results show that the proposed network achieved overall accuracy of 92.3%, and the comparison with other methods suggest the proposed network is able to map building roof with high accuracy.