Applying Bayesian Decision Classification to Pi-SAR Polarimetric Data for Detailed Extraction of the Geomorphologic and Structural Features of an Active Volcano

An understanding of the geomorphology and distribution of surface materials on an active volcano is crucial to characterize eruptions and mitigate volcanic hazards. For volcanoes, synthetic aperture radar (SAR) remote sensing is the only useful observation and monitoring technology that can be undertaken in any weather condition. This letter uses the data from one type of airborne SAR system termed polarimetric and interferometric airborne SAR and L-band microwaves to classify SAR imagery into geomorphologic units, based on a scattering mechanism, using the example of Mt. Sakurajima, a representative active volcano situated in southern Japan. This is accomplished by adopting a Bayesian decision classification (BDC) scheme applied to two polarimetric parameters, namely, entropy and the type of scattering mechanism, which are derived from Cloude-Pottier decomposition of full polarimetry. In spite of the thick vegetation cover, BDC can divide SAR imagery from Mt. Sakurajima into three geomorphologic units: volcanic cone, terrace, and foot. The suitability of the BDC classification of microwave sensor imagery-and its superiority over a traditional classification scheme, the K -means unsupervised classification-is confirmed by polarimetric signature analysis and ground-truth surveying that directly quantifies surface scattering.