A novel method is proposed to automatically measure the temporal correlation coeficient (TCC) of speckle from a set of SAR images of the same scene taken at different times. The knowledge of the TCC of speckle may expedite the detection and assessment of seasonal changes having occurred. In addition, it provides an upper bound to the maximum SNR increase achievable by multitemporal processing. A nonlinear transformation aimed at decorrelating the data across time, while retaining the multiplicative noise model, is defined. The TCC is estimated from the modes of the distributions of the local variation coefficient (C,) computed on the transformed images. Tests on SAR images (both synthetic and from ERS-1) show the accuracy and the robustness of the method, whose results are unaffected by underlying seasonal changes occurring across observations.
Evaluating Time Correlation of Speckle in ERS-1 SAR Images
L Alparone;S Baronti;A Garzelli
1998
Abstract
A novel method is proposed to automatically measure the temporal correlation coeficient (TCC) of speckle from a set of SAR images of the same scene taken at different times. The knowledge of the TCC of speckle may expedite the detection and assessment of seasonal changes having occurred. In addition, it provides an upper bound to the maximum SNR increase achievable by multitemporal processing. A nonlinear transformation aimed at decorrelating the data across time, while retaining the multiplicative noise model, is defined. The TCC is estimated from the modes of the distributions of the local variation coefficient (C,) computed on the transformed images. Tests on SAR images (both synthetic and from ERS-1) show the accuracy and the robustness of the method, whose results are unaffected by underlying seasonal changes occurring across observations.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
