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Hyper-spectral image nonlinear de-mixing method based on differential search

A hyperspectral image, nonlinear technology, applied in the field of nonlinear unmixing of hyperspectral images based on differential search, can solve the problems of low unmixing accuracy, large amount of calculation, complex parameters, etc., and achieve high unmixing accuracy and stability , overcome the effect of high initial value requirements and clear physical meaning

Inactive Publication Date: 2016-10-26
TIANJIN UNIV OF COMMERCE
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Problems solved by technology

However, the Bayesian estimation method has the disadvantages of complex parameters and large amount of calculation; the unmixing method based on the gradient method has problems such as easy to fall into local convergence and low unmixing accuracy
These problems make the hyperspectral image unmixing effect under this model still unsatisfactory.

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  • Hyper-spectral image nonlinear de-mixing method based on differential search
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  • Hyper-spectral image nonlinear de-mixing method based on differential search

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Embodiment Construction

[0035] Specific embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0036] combine figure 1 , figure 2 and image 3 The specific steps of the hyperspectral image nonlinear unmixing of the present invention are as follows (the flow chart of unmixing is as follows figure 1 shown):

[0037] Step 1 Input the actual hyperspectral image collected by the hyperspectral instrument, and use the VCA algorithm to extract endmembers from the image.

[0038] Moffett Field data is selected as the real scene data (such as figure 2 shown) and Japser Ridge data (as shown in image 3 shown) two kinds of hyperspectral image data.

[0039] (1) Moffett Field data

[0040] The data comes from hyperspectral imagery of Moffett Field at the southern tip of San Francisco Bay, California, USA. The image has 189 bands with a wavelength range of 400nm-2500nm and a spectral resolution of 10nm. In the experiment, a 50×50 sub-image is select...

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Abstract

The invention belongs to the technical field of hyper-spectral image processing, and especially relates to a hyper-spectral image nonlinear de-mixing method based on differential search. The method comprises the following steps: extracting end members of a real hyper-spectral image; determining the dimensionality and position code of each search individual; calculating the fitness value of each search individual according to an objective function; calculating a stop-over site i; comparing the fitness value of the current position Xi of each search individual with the fitness value of the stop-over site i thereof; and deciding whether to perform calculation according to conditions. According to the method, a nonlinear de-mixing problem is converted into an optimization problem, and the limitation that the traditional gradient optimization algorithm has high requirement on the initial value and easily falls into local convergence is overcome. Compared with a linear de-mixing algorithm and a gradient optimization-based nonlinear de-mixing algorithm, the hyper-spectral image nonlinear de-mixing method of the invention has the advantages of higher de-mixing precision and higher stability.

Description

technical field [0001] The invention belongs to the technical field of hyperspectral image processing, in particular to a hyperspectral image nonlinear unmixing method based on differential search. Background technique [0002] Hyperspectral image unmixing is an important task in hyperspectral image analysis and processing. Its purpose is to solve the problem of insufficient spatial resolution in the process of hyperspectral image capture. It has been widely used in remote sensing, materials science and microspectroscopy. multidisciplinary fields. Currently, most unmixing algorithms consider a mixed pixel to be a linear mixture of endmember spectral components. However, when there are ground objects such as soil, vegetation, and water in the hyperspectral image scene, the light will reflect and interact between different ground objects, and the unmixing algorithm based on the Linear Mixing Model (Linear Mixing Model, LMM) is often effective. Not ideal. One should try to s...

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Application Information

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IPC IPC(8): G06T1/00
CPCG06T1/00
Inventor 陈雷张立毅费腾张勇孙云山
Owner TIANJIN UNIV OF COMMERCE
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