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

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2 Author(s)
Deng, L. ; College of Resource Environment and Tourism, Capital Normal University, Beijing, China ; Wang, C.

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.

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Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of  (Volume:PP ,  Issue: 99 )

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