10 research outputs found
Unsupervised Classification of SAR Images using Hierarchical Agglomeration and EM
We implement an unsupervised classification algorithm for high resolution Synthetic Aperture Radar (SAR) images. The foundation of algorithm is based on Classification Expectation-Maximization (CEM). To get rid of two drawbacks of EM type algorithms, namely the initialization and the model order selection, we combine the CEM algorithm with the hierarchical agglomeration strategy and a model order selection criterion called Integrated Completed Likelihood (ICL). We exploit amplitude statistics in a Finite Mixture Model (FMM), and a Multinomial Logistic (MnL) latent class label model for a mixture density to obtain spatially smooth class segments. We test our algorithm on TerraSAR-X data
SAR image classification with non-stationary multinomial logistic mixture of amplitude and texture densities
We combine both amplitude and texture statistics of the Synthetic Aperture Radar (SAR) images using Products of Experts (PoE) approach for classification purpose. We use Nakagami density to model the class amplitudes. To model the textures of the classes, we exploit a non-Gaussian Markov Random Field (MRF) texture model with t-distributed regression error. Non-stationary Multinomial Logistic (MnL) latent class label model is used as a mixture density to obtain spatially smooth class segments. We perform the Classification Expectation-Maximization (CEM) algorithm to estimate the class parameters and classify the pixels. We obtained some classification results of water, land and urban areas in both supervised and semi-supervised cases on TerraSAR-X data
Correlated Component Analysis for diffuse component separation with error estimation on simulated Planck polarization data
We present a data analysis pipeline for CMB polarization experiments, running
from multi-frequency maps to the power spectra. We focus mainly on component
separation and, for the first time, we work out the covariance matrix
accounting for errors associated to the separation itself. This allows us to
propagate such errors and evaluate their contributions to the uncertainties on
the final products.The pipeline is optimized for intermediate and small scales,
but could be easily extended to lower multipoles. We exploit realistic
simulations of the sky, tailored for the Planck mission. The component
separation is achieved by exploiting the Correlated Component Analysis in the
harmonic domain, that we demonstrate to be superior to the real-space
application (Bonaldi et al. 2006). We present two techniques to estimate the
uncertainties on the spectral parameters of the separated components. The
component separation errors are then propagated by means of Monte Carlo
simulations to obtain the corresponding contributions to uncertainties on the
component maps and on the CMB power spectra. For the Planck polarization case
they are found to be subdominant compared to noise.Comment: 17 pages, accepted in MNRA
Joint Bayesian separation and restoration of CMB from convolutional mixtures
We propose a Bayesian approach to joint source separation and restoration for
astrophysical diffuse sources. We constitute a prior statistical model for the
source images by using their gradient maps. We assume a t-distribution for the
gradient maps in different directions, because it is able to fit both smooth
and sparse data. A Monte Carlo technique, called Langevin sampler, is used to
estimate the source images and all the model parameters are estimated by using
deterministic techniques.Comment: 11 pages, 6 figures. Submitted to MNRA
A Bayesian technique for the detection of point sources in CMB maps
The detection and flux estimation of point sources in cosmic microwave
background (CMB) maps is a very important task in order to clean the maps and
also to obtain relevant astrophysical information. In this paper we propose a
maximum a posteriori (MAP) approach detection method in a Bayesian scheme which
incorporates prior information about the source flux distribution, the
locations and the number of sources. We apply this method to CMB simulations
with the characteristics of the Planck satellite channels at 30, 44, 70 and 100
GHz. With a similar level of spurious sources, our method yields more complete
catalogues than the matched filter with a 5 sigma threshold. Besides, the new
technique allows us to fix the number of detected sources in a non-arbitrary
way.Comment: 9 pages, 9 figures. MNRAS accepted with major revision
The peak-constrained optimization of stable linear-phase IIR PRQMF bank
In this paper, we propose a new approach for optimization based stable linear-phase infinite impulse response (IIR) perfect reconstruction quadrature mirror filter (PRQMF) bank design. To this end, the design problem is formulated using stop-band energy, stability, transition-band, pass-band and stop-band ripples requirements as constraints. Lagrange multiplier method is used for the solution of optimization-based design problem. The brief conclusion with design example that illustrates the proposed design method is presented. (c) 2004 Elsevier GmbH. All rights reserved
The use of CNN for 2D two-channel DC IIR filter bank design
In this letter, our proposed approach exploits the use of original and simplest Cellular Neural Network (CNN) for 2D Doubly Complementary (DC) Infinite Impulse Response (IIR) filter banks design. The properties of feedback and feedforward templates are studied for this purpose. Through some examples it is shown how generalizations of these templates can be used for DC IIR filter banks design. We modify Lagrangian function which is used for optimizing a low-pass filter design considering the constraint for stability of CNN. The brief conclusions with design examples that illustrate the proposed method and an image enhancement and restoration applications of designed filter banks are presented