Idioma:

Lossless and Sufficient \Psi -Invariant Decomposition of Random Reciprocal Target

Paladini, R. ; Famil, L. Ferro ; Pottier, E. ; Martorella, M. ; Berizzi, F. ; Dalle Mese, Enzo

IEEE transactions on geoscience and remote sensing, 2012-09, Vol.50 (9), p.3487-3501 [Periódico revisado por pares]

New York, NY: IEEE

Texto completo disponível

Citações Citado por

Enviar para

Título:
Lossless and Sufficient \Psi -Invariant Decomposition of Random Reciprocal Target
Autor: Paladini, R. ; Famil, L. Ferro ; Pottier, E. ; Martorella, M. ; Berizzi, F. ; Dalle Mese, Enzo
Assuntos: Applied geophysics ; Automatic target classification ; automatic target recognition ; Classification ; Coherence ; Covariance matrix ; Decomposition ; Earth sciences ; Earth, ocean, space ; Eigenvalues and eigenfunctions ; Exact sciences and technology ; Internal geophysics ; Invariants ; Mathematical models ; Matrix decomposition ; Orientation ; polarimetry ; Polarization ; Radar ; radar cross section (RCS) ; Remote sensing ; synthetic aperture radar ; target decomposition ; target scattering ; Theorems ; Vectors
É parte de: IEEE transactions on geoscience and remote sensing, 2012-09, Vol.50 (9), p.3487-3501
Notas: ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
Descrição: The target coherency or covariance matrices are the main operators useful for characterizing the polarization transformation properties of radar target by modeling the depolarization effect. In this paper, a novel decomposition of the target coherency matrix is proposed, that is sufficient for representing the physical characteristics of the observed medium in term of a minimum set of orientation invariant parameters. The Einstein's photon circular polarization basis is used for obtaining an orientation invariant physical interpretation of the proposed parameter set both for deterministic and random target. A generalized unsupervised classification scheme is also proposed for underlining the effectiveness of the proposed decomposition theorem for classifying random reciprocal target into 75 physically meaningful clusters. The application of the proposed decomposition theorem and classification algorithm is useful for developing of novel Remote Sensing products and Data Mining softwares for monitoring the surfaces of the Earth and the Moon.
Editor: New York, NY: IEEE
Idioma: Inglês

Links

Voltar para lista de resultados

Anterior Resultado 9 Avançar Ir para próxima página

Realização: Logos de Redes Sociais:

Lossless and Sufficient \Psi -Invariant Decomposition of Random Reciprocal Target

Paladini, R. ; Famil, L. Ferro ; Pottier, E. ; Martorella, M. ; Berizzi, F. ; Dalle Mese, Enzo

New York, NY: IEEE

Buscando em bases de dados remotas. Favor aguardar.