English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Polarimetric synthetic aperture radar remote sensing extracts the information about the target using decomposition models to separate the polarimetric information into single-bounce (contributed by smooth surfaces), double-bounce (contributed by urban structure), and volume (mainly due to vegetation cover) scattering components. The penetration capacity of the electromagnetic wave into the surface increases with the decrease in its frequency. This study explores and compares the polarimetric decomposition models for scattering-based characterization of land use and cover using multifrequency spaceborne synthetic aperture radar sensor datasets that were acquired over San Francisco, CA, USA. The present work compares the scattering parameters of coherent (Pauli), roll-invariant (Barnes), eigenvalue–eigenvector (Cloude), and compact-polarimetric (Raney) decomposition modeling approaches for scattering-based characterization of urban structures, waterbody, and vegetation cover. The land use/cover classification was performed based on the scattering response of the scatterers using a support vector machine classifier. The outputs of the classification approach on multisensor, multifrequency, and multi-polarization polarimetric synthetic aperture radar data have shown reasonable accuracy in classifying the land use and land cover. The decomposition models fail to characterize the oriented urban structures that cause misclassification of urban structures as vegetation. The higher-order roll-invariant decomposition modeling approaches could improve the interpretation of different targets and accuracy in land use and land cover classification.

Multifrequency Spaceborne Synthetic Aperture Radar Data for Backscatter-Based Characterization of Land Use and Land Cover