Roads Detection of Aerial Image with FCN-CRF Model

摘要

This paper describes a deep learning based model for roads detection in Aerial image. In general, standard CNN networks would have less ability for tiny objects detection in remote sensing image. With this regard, we propose a novel fully convolutional network, which utilizes deconvolution layers and feature map fussing to take as input intensity and pixel-wise labeling. Moreover, the class prediction are used as the input to Condition Random Field (CRF) for the final pixel prediction. The Batch Normalization (BN) algorithm and two stages training strategy were used in our model to reduce the time cost of model training. Several experimental results conducted in Massachuseets. Road dataset demonstrate the superiority of our model with respect to accuracy and time cost.