Scalar and homoskedastic models for SAR and POLSAR data
SAR and POLSAR data are stochastic multiplicative and heteroskedastic in their natural domain. It is hence desirable to establish additive and homoskedastic models, such that the benefits of homoskedastic statistical estimation framework can be demonstrated and realized for practical applications su...
Main Author: | Le, Thanh-Hai |
---|---|
Other Authors: | Ian Vince McLoughlin |
Format: | Thesis |
Language: | English |
Published: |
2014
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/61772 |
Similar Items
-
Graphical models and variational Bayesian inference for financial networks
by: Xin, Luyin
Published: (2019) -
Urban data analytics for better power grid management
by: Selvarajoo, Stefan
Published: (2018) -
Pairwise copula cyclic graphical model for spatial extremes modeling
by: Zhu, Junting
Published: (2014) -
Differentiable generative models for trajectory data analytics
by: Li, Xiucheng
Published: (2020) -
Latent representation models for mining geo-spatial data
by: Liu, Yiding
Published: (2020)