Hyperspectral image classification method and device based on correlation coefficients and joint sparse representation

A hyperspectral image and correlation coefficient technology, applied in character and pattern recognition, instrument, scene recognition, etc., can solve problems such as single classification conditions, ignoring, ignoring pixel space fields, etc., to achieve good classification performance and overcome interference

Inactive Publication Date: 2020-03-17
HARBIN UNIV OF SCI & TECH
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Problems solved by technology

However, most of these methods only consider that pixels from the same class should have similar spectral features, while ignoring the spatial domain of the pixels, and most of these m

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  • Hyperspectral image classification method and device based on correlation coefficients and joint sparse representation
  • Hyperspectral image classification method and device based on correlation coefficients and joint sparse representation
  • Hyperspectral image classification method and device based on correlation coefficients and joint sparse representation

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[0059] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in combination with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are exemplary only, and are not intended to limit the scope of the present invention. Also, in the following description, descriptions of well-known structures and techniques are omitted to avoid unnecessarily obscuring the concept of the present invention.

[0060] Such as figure 1 , the present invention provides a hyperspectral image classification method based on correlation coefficient and joint sparse representation, the method steps are as follows:

[0061] Step S1: Divide an image data set into a training set and a test set;

[0062] Step S2: Calculate the size of the correlation coefficient between the training set and the test set by formula (1),

[0063]

[00...

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Abstract

The invention discloses a hyperspectral image classification method and device based on correlation coefficients and joint sparse representation, and belongs to the technical field of image classification. The hyperspectral image classification method comprises the steps: calculating the correlation between two variables through employing the correlation coefficients in combination with the similarity of spectrums and the local space consistency, so as to calculate the correlation between a pixel and each class; calculating a sparsest matrix by using an SOMP algorithm, and calculating a residual error between a pixel and each class by using the matrix; and moreover, constraining the classification function model by using the joint sparse representation and the correlation coefficient, andintroducing a parameter to balance the weights of the joint sparse representation and the correlation coefficient, so that the adaptability and robustness of the model are promoted, and compared withother methods, the hyperspectral image classification method is higher in accuracy and more stable in performance.

Description

technical field [0001] The invention belongs to the technical field of image classification, and in particular relates to a hyperspectral image classification method and device based on correlation coefficient and joint sparse representation. Background technique [0002] A spectral image with a spectral resolution within the range of 10l is called a hyperspectral image (HyperspectralImage). Through hyperspectral sensors mounted on different space platforms, that is, imaging spectrometers, in the ultraviolet, visible, near-infrared and mid-infrared regions of the electromagnetic spectrum, the target area is simultaneously imaged in tens to hundreds of continuous and subdivided spectral bands . While obtaining surface image information, it also obtains its spectral information, which is the first time that the spectrum and image are truly combined. Compared with multispectral remote sensing images, hyperspectral images have not only greatly improved in terms of information ...

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62
CPCG06V20/194G06V20/13G06V10/422G06V10/513G06F18/28G06F18/24
Inventor 李骜陈嘉佳丁宇陈德运孙广路林克正
Owner HARBIN UNIV OF SCI & TECH
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