In order to minimize uncertainty of the inversed parameters to the largest extent by making full use of the limited information in remote sensing data, it is necessary to understand what the information flow in quanti...In order to minimize uncertainty of the inversed parameters to the largest extent by making full use of the limited information in remote sensing data, it is necessary to understand what the information flow in quantitative remote sensing model inversion is, thus control the information flow. Aiming at this, the paper takes the linear kernel-driven model inversion as an example. At first, the information flow in different inversion methods is calculated and analyzed, then the effect of information flow controlled by multi-stage inversion strategy is studied, finally, an information matrix based on USM is defined to control information flow in inversion. It shows that using Shannon entropy decrease of the inversed parameters can express information flow more properly. Changing the weight of a priori knowledge in inversion or fixing parameters and partitioning datasets in multi-stage inversion strategy can control information flow. In regularization inversion of remote sensing, information matrix based on USM may be a better tool for quantitatively controlling information flow.展开更多
Signal and image restoration problems are often solved by minimizing a cost function consisting of an l2 data-fidelity term and a regularization term. We consider a class of convex and edge-preserving regularization f...Signal and image restoration problems are often solved by minimizing a cost function consisting of an l2 data-fidelity term and a regularization term. We consider a class of convex and edge-preserving regularization functions. In specific, half-quadratic regularization as a fixed-point iteration method is usually employed to solve this problem. The main aim of this paper is to solve the above-described signal and image restoration problems with the half-quadratic regularization technique by making use of the Newton method. At each iteration of the Newton method, the Newton equation is a structured system of linear equations of a symmetric positive definite coefficient matrix, and may be efficiently solved by the preconditioned conjugate gradient method accelerated with the modified block SSOR preconditioner. Our experimental results show that the modified block-SSOR preconditioned conjugate gradient method is feasible and effective for further improving the numerical performance of the half-quadratic regularization approach.展开更多
Geological structures often exhibit smooth characteristics away from sharp discontinuities. One aim of geophysical inversion is to recover information about the smooth structures as well as about the sharp discontinui...Geological structures often exhibit smooth characteristics away from sharp discontinuities. One aim of geophysical inversion is to recover information about the smooth structures as well as about the sharp discontinuities. Because no specific operator can provide a perfect sparse representation of complicated geological models, hyper-parameter regularization inversion based on the iterative split Bregman method was used to recover the features of both smooth and sharp geological structures. A novel preconditioned matrix was proposed, which counteracted the natural decay of the sensitivity matrix and its inverse matrix was calculated easily. Application of the algorithm to synthetic data produces density models that are good representations of the designed models. The results show that the algorithm proposed is feasible and effective.展开更多
基金This work was supported by the Special Funds for the Major State Basic Research Project(Grant No.G2000077903)the National Natural Science Foundation of China(Grant No.40171068).
文摘In order to minimize uncertainty of the inversed parameters to the largest extent by making full use of the limited information in remote sensing data, it is necessary to understand what the information flow in quantitative remote sensing model inversion is, thus control the information flow. Aiming at this, the paper takes the linear kernel-driven model inversion as an example. At first, the information flow in different inversion methods is calculated and analyzed, then the effect of information flow controlled by multi-stage inversion strategy is studied, finally, an information matrix based on USM is defined to control information flow in inversion. It shows that using Shannon entropy decrease of the inversed parameters can express information flow more properly. Changing the weight of a priori knowledge in inversion or fixing parameters and partitioning datasets in multi-stage inversion strategy can control information flow. In regularization inversion of remote sensing, information matrix based on USM may be a better tool for quantitatively controlling information flow.
基金supported by the China NSF Outstanding Young Scientist Foundation(No.10525102)National Natural Science Foundation(No.10471146)+3 种基金the National Basic Research Program (No.2005CB321702)P.R.Chinasupported in part by the Fundamental Research Fund for Physics and Mathematics of Lanzhou University.P.R.Chinasupported in part by Hong Kong Research Grants Council Grant Nos.7035/04P and 7035/05PHKBU FRGs
文摘Signal and image restoration problems are often solved by minimizing a cost function consisting of an l2 data-fidelity term and a regularization term. We consider a class of convex and edge-preserving regularization functions. In specific, half-quadratic regularization as a fixed-point iteration method is usually employed to solve this problem. The main aim of this paper is to solve the above-described signal and image restoration problems with the half-quadratic regularization technique by making use of the Newton method. At each iteration of the Newton method, the Newton equation is a structured system of linear equations of a symmetric positive definite coefficient matrix, and may be efficiently solved by the preconditioned conjugate gradient method accelerated with the modified block SSOR preconditioner. Our experimental results show that the modified block-SSOR preconditioned conjugate gradient method is feasible and effective for further improving the numerical performance of the half-quadratic regularization approach.
基金Projects(41174061,41374120)supported by the National Natural Science Foundation of China
文摘Geological structures often exhibit smooth characteristics away from sharp discontinuities. One aim of geophysical inversion is to recover information about the smooth structures as well as about the sharp discontinuities. Because no specific operator can provide a perfect sparse representation of complicated geological models, hyper-parameter regularization inversion based on the iterative split Bregman method was used to recover the features of both smooth and sharp geological structures. A novel preconditioned matrix was proposed, which counteracted the natural decay of the sensitivity matrix and its inverse matrix was calculated easily. Application of the algorithm to synthetic data produces density models that are good representations of the designed models. The results show that the algorithm proposed is feasible and effective.
