Title :
Rayleigh-Rice Mixture Parameter Estimation via EM Algorithm for Change Detection in Multispectral Images
Author :
Zanetti, Massimo ; Bovolo, Francesca ; Bruzzone, Lorenzo
Author_Institution :
Dept. of Inf. & Commun. Technol., Univ. of Trento, Trento, Italy
Abstract :
The problem of estimating the parameters of a Rayleigh-Rice mixture density is often encountered in image analysis (e.g., remote sensing and medical image processing). In this paper, we address this general problem in the framework of change detection (CD) in multitemporal and multispectral images. One widely used approach to CD in multispectral images is based on the change vector analysis. Here, the distribution of the magnitude of the difference image can be theoretically modeled by a Rayleigh-Rice mixture density. However, given the complexity of this model, in applications, a Gaussian-mixture approximation is often considered, which may affect the CD results. In this paper, we present a novel technique for parameter estimation of the Rayleigh-Rice density that is based on a specific definition of the expectation-maximization algorithm. The proposed technique, which is characterized by good theoretical properties, iteratively updates the parameters and does not depend on specific optimization routines. Several numerical experiments on synthetic data demonstrate the effectiveness of the method, which is general and can be applied to any image processing problem involving the Rayleigh-Rice mixture density. In the CD context, the Rayleigh-Rice model (which is theoretically derived) outperforms other empirical models. Experiments on real multitemporal and multispectral remote sensing images confirm the validity of the model by returning significantly higher CD accuracies than those obtained by using the state-of-the-art approaches.
Keywords :
Gaussian distribution; Gaussian processes; expectation-maximisation algorithm; image processing; mixture models; parameter estimation; remote sensing; CD; EM algorithm; Gaussian-mixture approximation; Rayleigh-Rice mixture density; Rayleigh-Rice mixture parameter estimation; change detection; change vector analysis; expectation-maximization algorithm; image magnitude distribution; multispectral remote sensing image analysis; multitemporal remote sensing image; Approximation methods; Change detection algorithms; Estimation; Image sensors; Remote sensing; Rician channels; Sensors; EM algorithm; Parameter estimation; Rayleigh distribution; Rician distribution; change detection; change vector analysis; multispectral images; remote sensing;
Journal_Title :
Image Processing, IEEE Transactions on
DOI :
10.1109/TIP.2015.2474710