Improved Building Extraction With Integrated Decomposition of Time-Frequency and Entropy-Alpha Using Polarimetric SAR Data

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Lei Deng¹  Cuizhen Wang² 

[1] College of Resource Environment and Tourism, Capital Normal University, Beijing, China;Department of Geography, University of Missouri, Columbia, MO, USA;

关键词: Building extraction;    entropy/alpha-Wishart classifier;    POLSAR;    time-frequency analysis;   

DOI  :  10.1109/JSTARS.2013.2281594

来源: DOAJ

【 摘 要 】

Building extraction is one of the primary applications of urban remote sensing. Polarimetric synthetic aperture radar (POLSAR), with its all-weather day and night imaging, canopy penetration and full polarimetric information, provides a unique way to detecting and characterizing urban areas. In this study, the time-frequency decomposition technique and the entropy/alpha-Wishart classifier were integrated to improve building extraction. The entropy/alpha-Wishart classifier was able to extract ortho-oriented buildings. After time-frequency transformation, the variation of entropy, alpha, anisotropy differs for objects with different scattering mechanisms, and the alpha angle of subaperture images was optimal in delineating slant-oriented buildings. A comparison between the integrated approach and the conventional entropy/alpha-Wishart classifier was performed on both C- and L-band NASA/JPL AIRSAR datasets. The overall accuracy and kappa value of the integrated approach was about 20% higher than that of the entropy/alpha-Wishart classifier. The C-band output tends to show more detailed scattering properties whereas the extracted buildings from the L-band image reveal better overall visual results.

【 授权许可】

Unknown