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Proceedings Paper

Texture analysis and despeckle of multitemporal SAR images
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Paper Abstract

Local-statistics speckle filtering has been extended to multitemporal SAR data by exploiting the temporal correlation of the speckle noise across a set of images of the same scene taken at different times. A recursive nonlinear transformation aimed at decorrelating the data across time, while retaining the multiplicative noise model, is defined from the geometric means and the ratios of couples of spatially overlapped observations. The temporal correlation coefficient (TCC) is estimated from the modes of the distributions of the local variation coefficient Cv computed on transformed couples of images. The images are filtered in the transformed domain and reversely transformed to yield despeckled observations in which seasonal changes are preserved, or even highlighted, and texture analysis is expedited. Tests on four SAR images from repeat-pass ERS-1 corroborate the theoretical assumptions and show the filtering performances of the proposed approach.

Paper Details

Date Published: 4 December 1998
PDF: 10 pages
Proc. SPIE 3500, Image and Signal Processing for Remote Sensing IV, (4 December 1998);
Show Author Affiliations
Luciano Alparone, Univ. of Florence (Italy)
Stefano Baronti, Istituto di Ricerca sulle Onde Elettromagnetiche Nello Carrara (Italy)
Roberto Carla, Istituto di Ricerca sulle Onde Elettromagnetiche Nello Carrara (Italy)

Published in SPIE Proceedings Vol. 3500:
Image and Signal Processing for Remote Sensing IV
Sebastiano Bruno Serpico, Editor(s)

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