Publication | Closed Access
Unsupervised Classification of Scattering Mechanisms in Polarimetric SAR Data Using Fuzzy Logic in Entropy and Alpha Plane
58
Citations
28
References
2007
Year
RadarFuzzy LogicAirborne Sar DataEngineeringFuzzy ClusteringSynthetic Aperture RadarAerospace EngineeringRadar ScatteringAlpha PlaneRemote SensingNew Classification SchemeRadar Image ProcessingRadar ApplicationRadar Signal ProcessingNew Classification TechniqueSignal ProcessingFuzzy Pattern RecognitionRadar Imaging
<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>
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