Land cover mapping method for polarimetric SAR data

Yosuke Ito; Sigeru Omatu

doi:10.1117/12.281577

28 July 1997 Land cover mapping method for polarimetric SAR data

Yosuke Ito, Sigeru Omatu

Proceedings Volume 3070, Algorithms for Synthetic Aperture Radar Imagery IV; (1997) https://doi.org/10.1117/12.281577
Event: AeroSense '97, 1997, Orlando, FL, United States

Abstract

We consider a land cover mapping method for polarimetric SAR data analysis. The method is based on a neural network whose input data are elements formed by the Stokes matrix. In this case, we must select a suitable combination of complex elements as a feature vector. After forming the probability density for each element and comparing the characteristics between JM distances, we determine a specific feature vector as the input for the network. As a result of experiments using SIR-C data, average accuracy for classification results is 86.40 percent, where (i) the 8D feature vector with backscattering coefficients and pseudo-phase differences between HH and VV from L and C bands and (ii) the competitive neural network with 8 input and 40 output neurons are simultaneously employed. In comparison, the proposed method outperforms other methods in average accuracy.

Citation Download Citation

Yosuke Ito and Sigeru Omatu "Land cover mapping method for polarimetric SAR data", Proc. SPIE 3070, Algorithms for Synthetic Aperture Radar Imagery IV, (28 July 1997); https://doi.org/10.1117/12.281577

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