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.
Fine co-registration of VHR images for multitemporal Urban area analysis / Han, Youkyung; Bovolo, Francesca; Bruzzone, Lorenzo. - ELETTRONICO. - (2015), pp. 1-4. (Intervento presentato al convegno Multitemp tenutosi a Annecy, France nel 21st July-23rd July 2015) [10.1109/Multi-Temp.2015.7245809].
Fine co-registration of VHR images for multitemporal Urban area analysis
Bovolo, Francesca;Bruzzone, Lorenzo
2015-01-01
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione