Robust terrain classification of high spatial resolution remote sensing data employing probabilistic feature fusion and pixelwise voting

R. Derek West; Brian J. Redman; David A. Yocky; John D. van der Laan; Dylan Z. Anderson

doi:10.1117/12.2558196

21 April 2020 Robust terrain classification of high spatial resolution remote sensing data employing probabilistic feature fusion and pixelwise voting

R. Derek West, Brian J. Redman, David A. Yocky, John D. van der Laan, Dylan Z. Anderson

Author Affiliations +

Proceedings Volume 11398, Geospatial Informatics X; 113980F (2020) https://doi.org/10.1117/12.2558196
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

There are several factors that should be considered for robust terrain classification. We address the issue of high pixel-wise variability within terrain classes from remote sensing modalities, when the spatial resolution is less than one meter. Our proposed method segments an image into superpixels, makes terrain classification decisions on the pixels within each superpixel using the probabilistic feature fusion (PFF) classifier, then makes a superpixel-level terrain classification decision by the majority vote of the pixels within the superpixel. We show that this method leads to improved terrain classification decisions. We demonstrate our method on optical, hyperspectral, and polarimetric synthetic aperture radar data.

Conference Presentation

Citation Download Citation

R. Derek West, Brian J. Redman, David A. Yocky, John D. van der Laan, and Dylan Z. Anderson "Robust terrain classification of high spatial resolution remote sensing data employing probabilistic feature fusion and pixelwise voting", Proc. SPIE 11398, Geospatial Informatics X, 113980F (21 April 2020); https://doi.org/10.1117/12.2558196

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