Non-negative matrix factorization method applied to hyperspectral image processing

A non-negative matrix decomposition, hyperspectral image technology, applied in the field of hyperspectral remote sensing image processing, can solve the problem of non-negative matrix decomposition algorithm iterative convergence slow local optimal solution and so on

Active Publication Date: 2015-10-14
南京博曼环保设备有限公司
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Problems solved by technology

However, the non-negative matrix factorization algorithm has the limitations of slow iterative convergence and easy to fall into a local optimal solution.

Method used

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  • Non-negative matrix factorization method applied to hyperspectral image processing
  • Non-negative matrix factorization method applied to hyperspectral image processing
  • Non-negative matrix factorization method applied to hyperspectral image processing

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Embodiment Construction

[0098] Below in conjunction with specific embodiment, further illustrate the present invention, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of the present application.

[0099] A non-negative matrix factorization method applied to hyperspectral image processing, comprising the following steps:

[0100] Step 1: Select a non-negative matrix V, and initialize W and H randomly;

[0101] Step 2: Iterate according to formula (6);

[0102] Step 3: Set the negative elements of W and H to zero after each step of iteration, and normalize W by column;

[0103] Step 4: Carry out steps 2-3 in a loop until convergence, at which point the base matrix W and the coefficient matr...

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Abstract

The present invention discloses a Non-negative Matrix Factorization (NMF) based on sparse and correlation constraints and the method is applied to processing of decomposition of mixed pixels of a hyper-spectral remote sensing image. According to the method, finally, a given non-negative matrix Vm*n is factorized into a product of a basis matrix Wm*r and a coefficient matrix Hr*n, i.e. Vm*n is approximately equal to Wm*r Hr*n ; firstly, the non-negative matrix V is selected, W and H are randomly initialized, then the minimum correlation constraint is applied to the coefficient matrix H in a target function, the sparse constraint is applied to the basis matrix W and the coefficient matrix H and then iteration is carried out according to an iteration formula until the matrices W and H are converged.

Description

technical field [0001] The invention relates to a hyperspectral image processing method, specifically a non-negative matrix factorization algorithm (Non-negative Matrix Factorization, NMF) based on sparsity and correlation constraints, and belongs to the technical field of hyperspectral remote sensing image processing. Background technique [0002] Remote sensing technology (Remote Sensing, RS) refers to the comprehensive observation technology of the earth's surface from long-distance space (aerospace) or outer space (aviation) that began in the 1960s. Remote sensing refers to indirect observation and monitoring from long-distance or outer space, without touching the target, to obtain relevant information about the target, phenomenon and area from the optical point of view, so as to carry out data fusion analysis and inference, and finally achieve the goal of obtaining the required target information. means, technology and science. Hyper-spectral image remote sensing techn...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00
CPCG06V20/194G06V20/13
Inventor 高红民李臣明王艳谢科伟陈玲慧史宇清
Owner 南京博曼环保设备有限公司
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