Rayleigh-Rice Mixture Parameter Estimation via EM Algorithm for Change Detection in Multispectral Images

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 change detection in multispectral images is based on Change Vector Analysis (CVA). 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 change detection 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 (EM) 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 change detection 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 change detection accuracies than those obtained by using state-of-the-art approaches.

Methods and Challenges Using Multispectral and Hyperspectral Images for Practical Change Detection Applications

“…In [26], alternative approaches are mentioned. Statistical methods such as [39,60] have been used in CVA analyses. Experimental results showed that the change detection results can almost approach optimal performance using some satellite images.…”

“…We will also review another interesting paper on direct subtraction. In [39], Zanetti et al analyzed the pixel magnitude distribution of the difference of two images collected at two times. The Rayleigh distribution can be used to model the magnitude of unchanged pixels, while the Rician distribution is for modeling the distribution of magnitude of change pixels.…”

Change Detection Based on Multi-Feature Clustering Using Differential Evolution for Landsat Imagery

“…The purpose of DE is to obtain CD clusters with the minimum fitness value. The fitness of individual G i can be calculated using Equation (13).…”

“…As the spatial resolution of the Landsat image is not high (≥15 m) and the intra-class variability in the image is small, pixel-based methods are suitable for Landsat image CD, which can be further divided into threshold-based or classification-based [3]. In the threshold-based method [9][10][11][12][13], it is crucial to analyze the statistical distribution of DI and find the optimal threshold to distinguish changed pixels from the unchanged ones. For example, Kittler and Illingworth proposed a minimum-error thresholding algorithm (KI), which determines the threshold by optimizing the average pixel classification error rate [11].…”

Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery

“…CVA is used together with unsupervised threshold selection techniques based on different possible models of the data distribution. For example, the Rayleigh-Rice mixture density model [9] has been recently used in the framework of the Expectation-Maximization (EM) algorithm.…”