关键词: 建筑物提取, 遥感图像, 密集连接, 卷积神经网络, 反卷积

Abstract: As remote sensing images cover wide ranges and contain complex scenes, the conventional building extraction methods based on convolution neural networks which have few layers, cannot fully excavate the abstract information of images. It results in low accuracy and high false detection rate. However, simply increasing the number of layers in the network will lead to the vanishing of the gradient flow and the dispersion of information flow. A new densely connected deep deconvolutional neural network is proposed in this paper, which introduces dense connections to deconvolution networks. This network which has 51 layers can automatically learn hierarchical features of images and can be trained end to end in order to avoid the vanishing of information flow in deep network. At the same time, with the deconvolution, it realizes building extraction in pixel level and has a good performance in extracting buildings. It works well in practice on the ISPRS 2D Semantic Labeling Contest data.

Key words: building extraction, remote sensing image, dense connection, convolutional neural network, deconvolution