Rayleigh-Rice Mixture Parameter Estimation via EM Algorithm for Change Detection in Multispectral Images
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TLDR
A novel technique for parameter estimation of the Rayleigh-Rice density that is based on a specific definition of the expectation-maximization algorithm is presented, which is characterized by good theoretical properties, iteratively updates the parameters and does not depend on specific optimization routines.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.read more
Citations
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Journal ArticleDOI
Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery
TL;DR: A novel recurrent convolutional neural network (ReCNN) architecture is proposed, which is trained to learn a joint spectral–spatial–temporal feature representation in a unified framework for change detection in multispectral images.
Journal ArticleDOI
A Review of Change Detection in Multitemporal Hyperspectral Images: Current Techniques, Applications, and Challenges
TL;DR: To fully exploit the available multitemporal HS images and their rich information content in change detection (CD), it is necessary to develop advanced automatic techniques that can address the complexity of the extraction of change information in an HS space.
Journal ArticleDOI
Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery
TL;DR: Wang et al. as mentioned in this paper proposed a novel recurrent convolutional neural network (ReCNN) architecture, which is trained to learn a joint spectral-spatial-temporal feature representation in a unified framework for change detection in multispectral images.
Journal ArticleDOI
Transferred Deep Learning-Based Change Detection in Remote Sensing Images
TL;DR: A transferred deep learning-based change detection framework that outperforms the state-of-the-art change detection methods via experimental results on real remote sensing data sets and demonstrates an excellent capacity for adapting the concept of change from the source domain to the target domain.
Journal ArticleDOI
Multiscale Morphological Compressed Change Vector Analysis for Unsupervised Multiple Change Detection
TL;DR: A novel multiscale morphological compressed change vector analysis method is proposed to address the multiple-change detection problem in bitemporal remote sensing images by jointly analyzing the spectral-spatial change information.
References
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Maximum likelihood from incomplete data via the EM algorithm
Book
A treatise on the theory of Bessel functions
TL;DR: The tabulation of Bessel functions can be found in this paper, where the authors present a comprehensive survey of the Bessel coefficients before and after 1826, as well as their extensions.
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Mathematical analysis of random noise
TL;DR: In this paper, the authors used the representations of the noise currents given in Section 2.8 to derive some statistical properties of I(t) and its zeros and maxima.
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