  With the advancement of remote-sensed imaging large volumes of very high
resolution land cover images can now be obtained. Automation of object
recognition in these 2D images, however, is still a key issue. High intra-class
variance and low inter-class variance in Very High Resolution (VHR) images
hamper the accuracy of prediction in object recognition tasks. Most successful
techniques in various computer vision tasks recently are based on deep
supervised learning. In this work, a deep Convolutional Neural Network (CNN)
based on symmetric encoder-decoder architecture with skip connections is
employed for the 2D semantic segmentation of most common land cover object
classes - impervious surface, buildings, low vegetation, trees and cars. Atrous
convolutions are employed to have large receptive field in the proposed CNN
model. Further, the CNN outputs are post-processed using Fully Connected
Conditional Random Field (FCRF) model to refine the CNN pixel label
predictions. The proposed CNN-FCRF model achieves an overall accuracy of 90.5%
on the ISPRS Vaihingen Dataset.
