Name
Title | ||
|---|---|---|
Kernel-based classification in complex-valued feature spaces for polarimetric SAR data |
Abstract | ||
|---|---|---|
A kernel-based approach is proposed in this paper to address supervised classification of polarimetric SAR data. Relevant features extracted from such data are generally complex-valued (e.g., scattering coefficients, multilook covariance-matrix entries). First, based on the theory of complex reproducing kernel Hilbert spaces (RKHS's), a family of admissible kernel functions tailored to the classification of complex-valued features is proposed. Then, a support vector machine (SVM) classifier is developed using this family of kernels and a case-specific interpretation is discussed for the related notion of maximum-margin hyperplane in a complex vector space. Finally, a spatial-contextual classifier is introduced by integrating the proposed family of kernels with a recent combination of SVM and Markov random fields. Case-specific techniques, based on the Powell and Ho-Kashyap numerical algorithms, are incorporated in the proposed methods to automatically optimize their parameters. Experiments with SIR-C data are discussed. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IGARSS.2014.6946661 | Geoscience and Remote Sensing Symposium |
Keywords | Field | DocType |
feature extraction,geophysical image processing,geophysical techniques,image classification,remote sensing by radar,synthetic aperture radar,Ho-Kashyap numerical algorithms,Markov random fields,PolSAR image,Powell numerical algorithms,SIR-C data,SVM classifier,admissible kernel functions,case-specific interpretation,case-specific techniques,complex vector space,complex-valued feature classification,complex-valued feature spaces,kernel Hilbert spaces,kernel-based approach,kernel-based classification,maximum-margin hyperplane,polarimetric SAR data,support vector machine,Markov random field (MRF),Polarimetric SAR,reproducing kernel Hilbert space (RKHS),support vector machine (SVM) | Kernel (linear algebra),Computer vision,Radial basis function kernel,Pattern recognition,Computer science,Kernel embedding of distributions,Support vector machine,Kernel principal component analysis,Polynomial kernel,Artificial intelligence,Kernel method,Kernel (statistics) | Conference |
ISSN | Citations | PageRank |
2153-6996 | 0 | 0.34 |
References | Authors | |
12 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriele Moser | 1 | 919 | 76.92 |
| Serpico, S.B. | 2 | 560 | 48.52 |