Detecting covariance symmetries for classification of polarimetric SAR images

Luca Pallotta, Carmine Clemente, Antonio De Maio, John J. Soraghan

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)
381 Downloads (Pure)


The availability of multiple images of the same scene acquired with the same radar but with different polarizations, both in transmission and reception, has the potential to enhance the classification, detection and/or recognition
capabilities of a remote sensing system. A way to take advantage of the full-polarimetric data is to extract, for each pixel of the considered scene, the polarimetric covariance matrix, coherence matrix, Muller matrix, and to
exploit them in order to achieve a specific objective. A framework for detecting covariance symmetries within polarimetric SAR images is here proposed. The considered algorithm is based on the exploitation of special structures assumed by the polarimetric coherence matrix under symmetrical properties of the returns associated with the pixels under test. The performance analysis of the technique is evaluated on both simulated and real L-band SAR data, showing a good classification level of the different areas within the image.
Original languageEnglish
Pages (from-to)80-95
Number of pages16
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number1
Early online date20 Oct 2016
Publication statusPublished - 31 Jan 2017


  • polarimetric SAR image
  • radar image classification
  • coherence matrix
  • covariance scattering matrix

Cite this