Height estimation from single aerial images using a deep convolutional encoder-decoder network

Abstract

Extracting 3D information from aerial images is an important and still challenging topic in photogrammetry and remote sensing. Height estimation from only a single aerial image is an ambiguous and ill-posed problem. To address this challenging problem, in this paper, an architecture based on a deep convolutional neural network (CNN) is proposed in order to estimate the height values from a single aerial image. Methodologies for data preprocessing, selection of training data as well as data augmentation are presented. Subsequently, a deep CNN architecture is proposed consisting of encoding and decoding steps. In the encoding part, a deep residual learning is employed for extracting the local and global features. An up-sampling approach is proposed in the decoding part for increasing the output resolution and skip connections are employed in each scale to modify the estimated height values at the object boundaries. Finally, a post-processing approach is proposed to merge the predicted height image patches and generate a seamless continuous height map. The quantitative evaluation of the proposed approaches on the ISPRS datasets indicates relative and root mean square errors of approximately 0.9 m and 3.2 m, respectively.