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A new polarimetric classification approach evaluated for agricultural crops

Dr. Dirk Hoekman (1)

(1) Wageningen University, Nieuwe Kanaal 11, 6709PA Wageningen, Netherlands

Abstract

Statistical properties of the polarimetric backscatter behaviour for a single homogeneous area are described by the Wishart distribution or its marginal distributions. These distributions do not necessarily well describe the statistics for a collection of homogeneous areas of the same class because of variation in, for example, biophysical parameters, which often is reflected in variation in the mean backscatter values. Using Kolmogorov-Smirnov (K-S) tests of fit it will be shown that, for example, the Beta distribution is a better descriptor for the coherence magnitude, and the log-normal distribution for the backscatter level. This will be evaluated for a number of agricultural crop classes, grasslands and fruit tree plantations at the Flevoland test site, using AirSAR (C-, L- and P-band polarimetric) data. Classification improvements will be quantified. Also the effect of azimuthal asymmetric backscatter behaviour on the classification results will be discussed. A new reversible transform of the covariance matrix will be introduced in order to describe the full polarimetric target properties in a mathematically simpler way, allowing even simpler statistical descriptions. It will be shown that this transform yields versatile and robust classification approaches and gives new insight in polarimetric decomposition. A comparison of results for the various classification methods will be given, using several (combinations) of frequency bands. To support generalisation of the results the physical relation between vegetation structure and backscatter mechanisms will be discussed explicitly.

 

Full paper

 

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