  Building footprint extraction (BFE) from multi-sensor data such as optical
images and light detection and ranging (LiDAR) point clouds is widely used in
various fields of remote sensing applications. However, it is still challenging
research topic due to relatively inefficient building extraction techniques
from variety of complex scenes in multi-sensor data. In this study, we develop
and evaluate a deep competition network (DCN) that fuses very high spatial
resolution optical remote sensing images with LiDAR data for robust BFE. DCN is
a deep superpixelwise convolutional encoder-decoder architecture using the
encoder vector quantization with classified structure. DCN consists of five
encoding-decoding blocks with convolutional weights for robust binary
representation (superpixel) learning. DCN is trained and tested in a big
multi-sensor dataset obtained from the state of Indiana in the United States
with multiple building scenes. Comparison results of the accuracy assessment
showed that DCN has competitive BFE performance in comparison with other deep
semantic binary segmentation architectures. Therefore, we conclude that the
proposed model is a suitable solution to the robust BFE from big multi-sensor
data.
