Remote sensing image fusion based on Gaussian mixture model and multiresolution analysis

2013 ◽  
Author(s):  
Moyan Xiao ◽  
Zhibiao He
Keyword(s):  
Remote Sensing ◽  
Image Fusion ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Multiresolution Analysis ◽  
Remote Sensing Image ◽  
Gaussian Mixture ◽  
Remote Sensing Image Fusion

scholarly journals Flexible Hierarchical Gaussian Mixture Model for High-Resolution Remote Sensing Image Segmentation

2020 ◽  
Vol 12 (7) ◽  
pp. 1219 ◽  
Author(s):  
Xue Shi ◽  
Yu Li ◽  
Quanhua Zhao
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
High Resolution ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Remote Sensing Image ◽  
Gaussian Mixture ◽  
Weighted Sums ◽  
Bayesian Theorem ◽  
Model Complex

The Gaussian mixture model (GMM) plays an important role in image segmentation, but the difficulty of GMM for modeling asymmetric, heavy-tailed, or multimodal distributions of pixel intensities significantly limits its application. One effective way to improve the segmentation accuracy is to accurately model the statistical distributions of pixel intensities. In this study, an innovative high-resolution remote sensing image segmentation algorithm is proposed based on a flexible hierarchical GMM (HGMM). The components are first defined by the weighted sums of elements, in order to accurately model the complicated distributions of pixel intensities in object regions. The elements of components are defined by Gaussian distributions to model the distributions of pixel intensities in local regions of the object region. Following the Bayesian theorem, the segmentation model is then built by combining the HGMM and the prior distributions of parameters. Finally, a novel birth or death Markov chain Monte Carlo (BDMCMC) is designed to simulate the segmentation model, which can automatically determine the number of elements and flexibly model complex distributions of pixel intensities. Experiments were implemented on simulated and real high-resolution remote sensing images. The results show that the proposed algorithm is able to flexibly model the complicated distributions and accurately segment images.

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