TY - GEN
T1 - Fine co-registration of VHR images for multitemporal Urban area analysis
AU - Han, Youkyung
AU - Bovolo, Francesca
AU - Bruzzone, Lorenzo
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/8
Y1 - 2015/9/8
N2 - Urban areas contain many manmade objects such as buildings and roads that create a huge amount of salient features in Very High Resolution (VHR) images. These features are often used as Control Points (CPs) in state-of-the-art co-registration approaches. However, a large number of CPs especially clustered together may result in a poor matching between multitemporal images and thus a poor co-registration performance. In order to effectively reduce the number of CPs and achieve good co-registration performance, we propose a context-based CPs selection approach. To this end, context-based CPs are extracted by applying a segmentation method. Their correspondences are established by considering local misalignment, also called Registration Noise (RN). Thus the approach achieves fine co-registration performance even in complex scenarios like urban areas. The experiments on both a simulated and a real dataset confirmed the effectiveness of the proposed approach.
AB - Urban areas contain many manmade objects such as buildings and roads that create a huge amount of salient features in Very High Resolution (VHR) images. These features are often used as Control Points (CPs) in state-of-the-art co-registration approaches. However, a large number of CPs especially clustered together may result in a poor matching between multitemporal images and thus a poor co-registration performance. In order to effectively reduce the number of CPs and achieve good co-registration performance, we propose a context-based CPs selection approach. To this end, context-based CPs are extracted by applying a segmentation method. Their correspondences are established by considering local misalignment, also called Registration Noise (RN). Thus the approach achieves fine co-registration performance even in complex scenarios like urban areas. The experiments on both a simulated and a real dataset confirmed the effectiveness of the proposed approach.
KW - Context-based Control Points (CPs)
KW - Registration Noise (RN)
KW - urban area analysis
KW - Very High Resolution (VHR) images
UR - https://www.scopus.com/pages/publications/84959884725
U2 - 10.1109/Multi-Temp.2015.7245809
DO - 10.1109/Multi-Temp.2015.7245809
M3 - Conference contribution
AN - SCOPUS:84959884725
T3 - 2015 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images, Multi-Temp 2015
BT - 2015 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images, Multi-Temp 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images, Multi-Temp 2015
Y2 - 22 July 2015 through 24 July 2015
ER -