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In consideration of some problems including the independence and invariance of components, no noise assumption and the uncertainty of the final solution as well as the inconsistency of the features of remote sensing data in traditional independent component analysis (ICA) model, we put forward a blind source separation algorithm for remote sensing images using variational Bayesian ICA. In the proposed method, the Bayesian network is introduced into the ICA model, the Bayesian inference is used to complete the study of unknown hidden variables, and the computation is optimized by combination with the variational approximation method. Finally, the proposed method is validated by simulation and real remote sensing image tests. The result shows that the variational Bayesian ICA algorithm has both good stability and separation effect, and it overcomes the deficiency of the traditional ICA method in remote sensing application. Introduction Remote sensing technology has the advantages of wide coverage, large amount of information and short revisit cycle. It is able to obtain the change information of the surface of the earth timely and accurately, and widely applied in the resource investigation, environmental monitoring, etc. [1, 2] Compared with the traditional digital image, the remote sensing image contains more diverse terrain types, also the distribution is more complex. As a new signal processing technology, ICA has close association with the problem of blind source separation, and it has the ability of separating source signals (terrain components) from the mixed ones without the need of prior knowledge [3-5]. At present, the application field of the ICA has expanded from solution of mixed blind signals to signal detection, image processing and pattern recognition, etc. But the ICA model has problems containing the independence and invariance of components, no noise assumption as well as the uncertainty of the final solution [6]. These assumptions of the ICA make it not completely consistent with the actual state of remote sensing image. This paper puts forward blind source separation for remote sensing images based on improved ICA algorithm, i.e. variational Bayesian ICA method, the algorithm is based on the analysis of the traditional ICA model and of the characteristics of remote sensing image. In the proposed method, the Bayesian network is introduced into the ICA model, we used Bayesian inference to complete the study of unknown hidden variables (independent components), and we optimized it by combination with the variational approximation algorithm to make the isolated independent components as consistent as the real situation of the surface. This paper analyzed the variational Bayesian ICA method from the aspects of both simulation images and real remote sensing image respectively, and the results of experiments verified the effectiveness of the proposed algorithm. Variational Bayesian ICA Algorithm Add noise information in the standard ICA model, making it the linear mixed ICA model with noise [7, 8], and the formula is shown below: ( ) ( ) ( ) x t As t t ε = + (1) Where the x(t) refers to a mixed signal with M, s(t) refers to a source signal (hidden variable) with L, International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2015) © 2015. The authors Published by Atlantis Press 788 mixed matrix A is M × L, ε(t) refers to Gaussian noise, which usually is a diagonal matrix with the inverse variance for Λ and the mean for zero. In linear mixed ICA model containing noises, the probability calculation formula for mixed signal x(t) is: 1 2 1 ( | , , ) | det( ) | exp[ ] 2 D p x s A E p Λ = Λ − (2) Where 1 ( ) ( ) 2 T D E x As x As = − Λ − , det(·) refers to the determinant value. In the model we assume the source signals are independent of each other, so the probability distribution formula for s can be expressed as 1 ( ) ( ) L

Blind Source Separation for Remote Sensing Images based on the Improved ICA Algorithm