TY - GEN
T1 - Efficient joint stereo estimation and land usage classification for multiview satellite data
AU - Wang, Ke
AU - Stutts, Craig
AU - Dunn, Enrique
AU - Frahm, Jan Michael
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/5/23
Y1 - 2016/5/23
N2 - We propose an efficient algorithm to jointly estimate geometry and semantics for a given geographical region observed by multiple satellite images. Our joint estimation leverages an efficient PatchMatch inference framework defined over lattice discretization of the environment. Our cost function relies on the local planarity assumption to model scene geometry and neural network classification to determine semantic (e.g. land use) labels for geometric structures. By utilizing the commonly available direct (i.e. space to image) rational polynomial coefficients (RPC) satellite camera models, our approach effectively circumvents the need for estimating or refining inverse RPC models. Experiments illustrate both the computational efficiency and high quality scene geometry estimates attained by our approach for satellite imagery. To further illustrate the generality of our representation and inference framework, experiments on standard benchmarks for ground-level imagery are also included.
AB - We propose an efficient algorithm to jointly estimate geometry and semantics for a given geographical region observed by multiple satellite images. Our joint estimation leverages an efficient PatchMatch inference framework defined over lattice discretization of the environment. Our cost function relies on the local planarity assumption to model scene geometry and neural network classification to determine semantic (e.g. land use) labels for geometric structures. By utilizing the commonly available direct (i.e. space to image) rational polynomial coefficients (RPC) satellite camera models, our approach effectively circumvents the need for estimating or refining inverse RPC models. Experiments illustrate both the computational efficiency and high quality scene geometry estimates attained by our approach for satellite imagery. To further illustrate the generality of our representation and inference framework, experiments on standard benchmarks for ground-level imagery are also included.
UR - http://www.scopus.com/inward/record.url?scp=84977626038&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84977626038&partnerID=8YFLogxK
U2 - 10.1109/WACV.2016.7477657
DO - 10.1109/WACV.2016.7477657
M3 - Conference contribution
AN - SCOPUS:84977626038
T3 - 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016
BT - 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016
T2 - IEEE Winter Conference on Applications of Computer Vision, WACV 2016
Y2 - 7 March 2016 through 10 March 2016
ER -