A novel method is proposed to automatically measure the temporal correlation coefficient (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-nu) 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 / Alparone, Luciano; Baronti, Stefano; Garzelli, Andrea. - STAMPA. - 1:(1998), pp. 30-32. (Intervento presentato al convegno Proceedings of the 1998 IEEE International Geoscience and Remote Sensing Symposium, IGARSS. Part 1 (of 5) tenutosi a Seattle, WA, USA nel 6 - 10 July 1998) [10.1109/IGARSS.1998.702787].

Evaluating time correlation of speckle in ERS-1 SAR images

ALPARONE, LUCIANO;
1998

Abstract

A novel method is proposed to automatically measure the temporal correlation coefficient (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-nu) 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.
1998
International Geoscience and Remote Sensing Symposium (IGARSS)
Proceedings of the 1998 IEEE International Geoscience and Remote Sensing Symposium, IGARSS. Part 1 (of 5)
Seattle, WA, USA
6 - 10 July 1998
Alparone, Luciano; Baronti, Stefano; Garzelli, Andrea
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1075604
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