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BAYESIAN FUSION OF HYPERSPECTRAL AND MULTISPECTRAL IMAGES
"... This paper presents a Bayesian fusion technique for multiband images. The observed images are related to the high spectral and high spatial resolution image to be recovered through physical degradations, e.g., spatial and spectral blurring and/or subsampling defined by the sensor characteristics. ..."
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This paper presents a Bayesian fusion technique for multiband images. The observed images are related to the high spectral and high spatial resolution image to be recovered through physical degradations, e.g., spatial and spectral blurring and/or subsampling defined by the sensor characteristics. The fusion problem is formulated within a Bayesian estimation framework. An appropriate prior distribution related to the linear mixing model for hyperspectral images is introduced. To compute Bayesian estimators of the scene of interest from its posterior distribution, a Gibbs sampling algorithm is proposed to generate samples asymptotically distributed according to the target distribution. To efficiently sample from this highdimensional distribution, a Hamiltonian Monte Carlo step is introduced in this Gibbs sampler. The efficiency of the proposed fusion method is evaluated with respect to several stateoftheart fusion techniques. Index Terms — Fusion, multispectral and hyperspectral images, Bayesian estimation, Gibbs sampler, Hamiltonian Monte Carlo. 1.
KnowledgeAided Adaptive Coherence Estimator in Stochastic Partially Homogeneous Environments
, 2011
"... This paper introduces a stochastic partially homogeneous model for adaptive signal detection. In this model, the disturbance covariance matrix of training signals, R, is assumed to be a random matrix with some a priori information, while the disturbance covariance matrix of the test signal, R0, is a ..."
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This paper introduces a stochastic partially homogeneous model for adaptive signal detection. In this model, the disturbance covariance matrix of training signals, R, is assumed to be a random matrix with some a priori information, while the disturbance covariance matrix of the test signal, R0, is assumed to be equal to R, i.e., R0 = R. On one hand, this model extends the stochastic homogeneous model by introducing an unknown power scaling factor between the test and training signals. On the other hand, it can be considered as a generalization of the standard partially homogeneous model to the stochastic Bayesian framework, which treats the covariance matrix as a random matrix. According to the stochastic partially homogeneous model, a scaleinvariant generalized likelihood ratio test (GLRT) for the adaptive signal detection is developed, which is a knowledgeaided version of the wellknown adaptive coherence estimator (ACE). The resulting knowledgeaided ACE (KAACE) employs a colored loading step utilizing the a priori knowledge and the sample covariance matrix. Various simulation results and comparison with respect to other detectors confirm the scaleinvariance and the effectiveness of the KAACE.
Bayesian Fusion of MultiBand Images Complementary results and supporting materials
"... Abstract In this paper, a Bayesian fusion technique for remotely sensed multiband images is presented. The observed images are related to the high spectral and high spatial resolution image to be recovered through physical degradations, e.g., spatial and spectral blurring and/or subsampling define ..."
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Abstract In this paper, a Bayesian fusion technique for remotely sensed multiband images is presented. The observed images are related to the high spectral and high spatial resolution image to be recovered through physical degradations, e.g., spatial and spectral blurring and/or subsampling defined by the sensor characteristics. The fusion problem is formulated within a Bayesian estimation framework. An appropriate prior distribution exploiting geometrical consideration is introduced. To compute the Bayesian estimator of the scene of interest from its posterior distribution, a Markov chain Monte Carlo algorithm is designed to generate samples asymptotically distributed according to the target distribution. To efficiently sample from this highdimension distribution, a Hamiltonian Monte Carlo step is introduced in the Gibbs sampling strategy. The efficiency of the proposed fusion method is evaluated with respect to several stateoftheart fusion techniques. In particular, low spatial resolution hyperspectral and multispectral images are fused to produce a high spatial resolution hyperspectral image. Index Terms Part of this work has been supported by the Hypanema ANR Project n
Research Article Rao and Wald Tests for Adaptive Detection in Partially Homogeneous Environment with a Diversely Polarized Antenna
"... Copyright © 2013 Chaozhu Zhang et al.This is an open access article distributed under the Creative CommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This study considers Rao test and Wald test for a ..."
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Copyright © 2013 Chaozhu Zhang et al.This is an open access article distributed under the Creative CommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This study considers Rao test and Wald test for adaptive detection based on a diversely polarized antenna (DPA) in partially homogeneous environment. The theoretical expressions for the probability of false alarm and detection are derived, and constant false alarm rate (CFAR) behaviour is remarked on. Furthermore, the monotonicities of detection probability of the two detectors are proved, and a polarization optimization detection algorithm to enhance the detection performance is proposed.The numerical simulations are conducted to attest to the validity of the above theoretical analysis and illustrate the improvement in the detection performance of the proposed optimization algorithm. 1.