A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
An Automatic -Distribution and Markov Random Field Segmentation Algorithm for PolSAR Images
We have recently presented a novel unsupervised, non-Gaussian, and contextual clustering algorithm for segmentation of polarimetric synthetic aperture radar (PolSAR) images. This represents one of the most advanced PolSAR unsupervised statistical segmentation algorithms and uses the doubly flexible two-parameter U-distribution model for the PolSAR statistics and includes a Markov random field (MRF) approach for contextual smoothing. A goodness-of-fit testing stage adds a statistically rigorous approach to determine the significant number of classes. The fully automatic algorithm was demonstrated with good results for both simulated and real data sets. This paper discusses a rethinking of the overall strategy and leads to some simplifications. The primary issue was that the MRF optimization depends on the number of classes and did not behave well under the split-and-merge environment. We explain the reasons behind a separation of the cluster evaluation from the contextual smoothing and a modified rationale for the adaptive number of classes. Both aspects have simplified the overall algorithm while maintaining good visual results.
An Automatic -Distribution and Markov Random Field Segmentation Algorithm for PolSAR Images
We have recently presented a novel unsupervised, non-Gaussian, and contextual clustering algorithm for segmentation of polarimetric synthetic aperture radar (PolSAR) images. This represents one of the most advanced PolSAR unsupervised statistical segmentation algorithms and uses the doubly flexible two-parameter U-distribution model for the PolSAR statistics and includes a Markov random field (MRF) approach for contextual smoothing. A goodness-of-fit testing stage adds a statistically rigorous approach to determine the significant number of classes. The fully automatic algorithm was demonstrated with good results for both simulated and real data sets. This paper discusses a rethinking of the overall strategy and leads to some simplifications. The primary issue was that the MRF optimization depends on the number of classes and did not behave well under the split-and-merge environment. We explain the reasons behind a separation of the cluster evaluation from the contextual smoothing and a modified rationale for the adaptive number of classes. Both aspects have simplified the overall algorithm while maintaining good visual results.
An Automatic -Distribution and Markov Random Field Segmentation Algorithm for PolSAR Images
Doulgeris, Anthony P (author)
2015
Article (Journal)
English
Local classification TIB:
770/3710/5670
BKL:
38.03
Methoden und Techniken der Geowissenschaften
/
74.41
Luftaufnahmen, Photogrammetrie
An automatic calU -distribution and Markov random field segmentation algorithm for PolSAR images
| Online Contents | 2015
Representation and Spatially Adaptive Segmentation for PolSAR Images Based on Wedgelet Analysis
| Online Contents | 2015
Spherical Symmetry of Complex Stochastic Models in Multivariate High-Resolution PolSAR Images
| Online Contents | 2016