Litcius/Paper detail

Exploring Fine Polarimetric Decomposition Technique for Built-Up Area Monitoring

Sinong Quan, Tao Zhang, Wei Wang, Gangyao Kuang, Xuesong Wang, Bing Zeng

2023IEEE Transactions on Geoscience and Remote Sensing30 citationsDOI

Abstract

Highly variable polarimetric signatures caused by complex structures in built-up areas make interpretation of these scattering behaviors intractable for PolSAR remote sensing. This paper proposes a fine polarimetric decomposition method and derives several products to finely simulate the scattering mechanisms of urban buildings, thus fulfilling its use for effective surveillance. First, through theoretically establishing the roll-invariant condition for a completely general scatterer, a roll-invariant cross polarization (RICP) scattering model is constructed, which characterizes the cross polarization scattering in the manner of planar structure distribution. Second, by designing a root-discriminant-based parameter inversion strategy, a fine seven-component decomposition is proposed, which achieves the complete physical interpretation of matrix elements and reasonable inversion of model parameters. Third, by analyzing the external and internal scattering difference, the derivative products, i.e., scattering contribution synthesizers are derived for built-up area monitoring. Experimental results derived from real PolSAR data confirm the superiority and effectiveness of the constructed descriptors on the one hand. On the other hand, the extensibility of fine polarimetric decomposition in specific remote sensing is also explicitly demonstrated.

Topics & Concepts

ScatteringPolarimetryComputer scienceRemote sensingMatrix decompositionInversion (geology)Polarization (electrochemistry)DiscriminantAlgorithmArtificial intelligenceOpticsGeologyPhysicsEigenvalues and eigenvectorsPhysical chemistryQuantum mechanicsChemistryPaleontologyStructural basinSynthetic Aperture Radar (SAR) Applications and TechniquesRemote-Sensing Image ClassificationSoil Moisture and Remote Sensing