Texture analysis and despeckle of multitemporal SAR images

Luciano Alparone; Stefano Baronti; Roberto Carla

doi:10.1117/12.331857

4 December 1998 Texture analysis and despeckle of multitemporal SAR images

Luciano Alparone, Stefano Baronti, Roberto Carla

Proceedings Volume 3500, Image and Signal Processing for Remote Sensing IV; (1998) https://doi.org/10.1117/12.331857
Event: Remote Sensing, 1998, Barcelona, Spain

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 C_v 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.

Citation Download Citation

Luciano Alparone, Stefano Baronti, and Roberto Carla "Texture analysis and despeckle of multitemporal SAR images", Proc. SPIE 3500, Image and Signal Processing for Remote Sensing IV, (4 December 1998); https://doi.org/10.1117/12.331857

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