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Nonlinear un-mixing method of hyperspectral images based on kernel sparse nonnegative matrix decomposition

A non-negative matrix decomposition, hyperspectral image technology, applied in the field of remote sensing information processing, can solve problems such as unconsidered, achieve the effect of solving nonlinear unmixing, good anti-noise performance, and overcoming linear unmixing

Inactive Publication Date: 2015-03-04
扬州匠新精密数控设备有限公司
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Problems solved by technology

Because it is an unmixing method based on a linear model, the influence of the actual natural environment on hyperspectral unmixing is not considered. Due to the existence of nonlinear mixing phenomena, such methods will not get accurate results

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  • Nonlinear un-mixing method of hyperspectral images based on kernel sparse nonnegative matrix decomposition
  • Nonlinear un-mixing method of hyperspectral images based on kernel sparse nonnegative matrix decomposition
  • Nonlinear un-mixing method of hyperspectral images based on kernel sparse nonnegative matrix decomposition

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Embodiment Construction

[0037] Now in conjunction with embodiment, accompanying drawing, the present invention will be further described:

[0038] An embodiment of the present invention is a hyperspectral image nonlinear unmixing method based on kernel sparse non-negative matrix decomposition, and the specific steps are as follows:

[0039] Step 1 Input the hyperspectral data to be unmixed Where L is the number of bands and N is the number of pixels.

[0040] Step 2 uses the virtual dimension approach to The number of endmembers is estimated to be P in the hyperspectral image.

[0041] Step 3: Input the unmixing parameters: respectively input the number of endmembers P, the coefficients λ and β, the kernel parameters σ and the minimum convergence residual threshold τ.

[0042] Step 4 Construct the following kernel function matrix K

[0043] K = Φ ( X ) · Φ ( ...

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Abstract

The invention relates to a nonlinear un-mixing method of hyperspectral images based on kernel sparse nonnegative matrix decomposition. The nonlinear un-mixing method comprises the following steps: estimating the number of end members for hyperspectral images by utilizing a dimension virtual method; then, popularizing the conventional un-mixing algorithm based on a linear mixing model to a nonlinear characteristic space by utilizing a kernel method, and solving a nonlinear spectrum un-mixing problem by using an alternative iterative optimization method. The nonlinear un-mixing method of hyperspectral images based on kernel sparse nonnegative matrix decomposition has the beneficial effects that from a mixing model of hyperspectral observation pixels, the sparsity of the hyperspectral abundance is added into a sparse model, and the linear mixing model is mapped into a nonlinear mixing model by virtue of the kernel method, so that the defects of linear un-mixing are effectively overcome, and good noise resistances are simultaneously achieved, and therefore, the nonlinear un-mixing method can be used as an effective means for solving the un-mixing of hyperspectral remote sensing images.

Description

technical field [0001] The invention belongs to the technical field of remote sensing information processing, and in particular relates to a hyperspectral image nonlinear unmixing method based on kernel sparse non-negative matrix decomposition. Background technique [0002] Hyperspectral remote sensing images are widely used in remote sensing applications due to their advantages of high spectral resolution and multiple imaging bands. Due to the generally low spatial resolution and the complex diversity of natural objects, an observed pixel is likely to contain more than one type of object, and this pixel is called a mixed pixel. Mixed pixels commonly exist in hyperspectral remote sensing images. Therefore, the technology of analyzing the proportion of various types of ground objects in mixed pixels, that is, spectral unmixing technology, is the prerequisite for accurate classification and identification of ground objects. The mixed pixel decomposition methods of hyperspectr...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/29
Inventor 李映房蓓
Owner 扬州匠新精密数控设备有限公司
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