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Unsupervised Classification of Scattering Mechanisms in Polarimetric SAR Data Using Fuzzy Logic in Entropy and Alpha Plane

58

Citations

28

References

2007

Year

Abstract

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> The eigenvalue-eigenvector-based approach for understanding the scattering mechanisms of polarimetric synthetic aperture radar (POLSAR) data leads to noisy classification results due to arbitrarily fixed zone boundaries in the <formula formulatype="inline"><tex>$H/\overline{\alpha}$</tex></formula> plane. In this paper, a new classification scheme that can address the inherent vagueness of class boundaries in the <formula formulatype="inline"><tex>$H/\overline{\alpha}$</tex></formula> plane was tested in order to improve the unsupervised classification of the microwave scattering mechanism by introducing concepts related to fuzzy sets. A 2-D fuzzy membership function was developed for the fuzzification of the 2-D <formula formulatype="inline"><tex>$H/ \overline{\alpha}$</tex></formula> plane. The proposed fuzzy <formula formulatype="inline"><tex>$H/\overline{ \alpha}$</tex></formula> classifier is composed of three steps: fuzzification of the <formula formulatype="inline"> <tex>$H/\overline{\alpha}$</tex></formula> plane, iterative refinement of membership degrees using the <formula formulatype="inline"><tex>$c$</tex></formula>-means algorithm, and defuzzification for the final decision process. The performance of this new approach for the L-band NASA/Jet Propulsion Laboratory's Airborne SAR data obtained during the PACRIM-II experiment was shown to be consistently improved. This new classification technique can be applied to POLSAR data without any <emphasis emphasistype="boldital">a priori</emphasis> information. The fuzzification of the zone boundaries can be further applied to the interpretation of the POLSAR data, e.g., multifrequency classification, retrieval of bio- and geophysical parameters, etc. In order to propose another implementation of the fuzzy boundary representation, we exploited the combination of the <formula formulatype="inline"><tex>$H/\overline{\alpha}$</tex></formula> state space and anisotropy information. </para>

References

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