Even after co-registration, Very High Resolution (VHR) multitemporal images acquired by different multispectral sensors (e.g., QuickBird, WordView) show a residual misregistration due to dissimilarities in acquisition conditions and in sensor properties. Residual misregistration can be considered as a source of noise and is referred to as Registration Noise (RN). Since RN is likely to have a negative impact on multitemporal information extraction, detecting and reducing it can increase multitemporal image processing accuracy. In this paper, we propose an approach to identify RN between VHR multitemporal and multisensor images. Under the assumption that dominant RN mainly exists along boundaries of objects, we propose to use edge information in high frequency regions to estimate it. This choice makes RN detection less dependent on radiometric differences and thus more effective in VHR multisensor image processing. In order to validate the effectiveness of the proposed approach, multitemporal multisensor datasets are built including QuickBird and WorldView VHR images. Both qualitative and quantitative assessments demonstrate the effectiveness of the proposed RN identification approach compared to the state-of-the-art one.
Edge-Based Registration-Noise Estimation in VHR Multitemporal and Multisensor Images
Han, Youkyung;Bovolo, Francesca;
2016-01-01
Abstract
Even after co-registration, Very High Resolution (VHR) multitemporal images acquired by different multispectral sensors (e.g., QuickBird, WordView) show a residual misregistration due to dissimilarities in acquisition conditions and in sensor properties. Residual misregistration can be considered as a source of noise and is referred to as Registration Noise (RN). Since RN is likely to have a negative impact on multitemporal information extraction, detecting and reducing it can increase multitemporal image processing accuracy. In this paper, we propose an approach to identify RN between VHR multitemporal and multisensor images. Under the assumption that dominant RN mainly exists along boundaries of objects, we propose to use edge information in high frequency regions to estimate it. This choice makes RN detection less dependent on radiometric differences and thus more effective in VHR multisensor image processing. In order to validate the effectiveness of the proposed approach, multitemporal multisensor datasets are built including QuickBird and WorldView VHR images. Both qualitative and quantitative assessments demonstrate the effectiveness of the proposed RN identification approach compared to the state-of-the-art one.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
