Polarimetric segmentation of SAR imagery

Michael C. Burl; Leslie M. Novak

doi:10.1117/12.44870

1 August 1991 Polarimetric segmentation of SAR imagery

Michael C. Burl, Leslie M. Novak

Proceedings Volume 1471, Automatic Object Recognition; (1991) https://doi.org/10.1117/12.44870
Event: Orlando '91, 1991, Orlando, FL, United States

Abstract

This paper considers the problem of clutter segmentation in fully polarimetric, high-resolution, synthetic aperture radar (SAR) imagery. The goal of segmentation is to partition an image into regions of homogeneous terrain types (grass regions, tree regions, roads, etc.). Three approaches to segmentation are examined: (1) the optimal polarimetric classifier, (2) the optimal normalized polarimetric classifier, and (3) the polarimetric whitening filter (PWF) classifier. Segmentation performance results are presented for typical high-resolution, polarimetric SAR data gathered by the Lincoln Laboratory 35-GHz airborne sensor.

Citation Download Citation

Michael C. Burl and Leslie M. Novak "Polarimetric segmentation of SAR imagery", Proc. SPIE 1471, Automatic Object Recognition, (1 August 1991); https://doi.org/10.1117/12.44870

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