Image semi-supervised classification method and device and computer readable storage medium

A classification method and a semi-supervised technology, applied to computer components, calculations, instruments, etc., can solve problems such as single neighbor attributes, strong connection noise, and easy to appear isolated points, etc., to achieve accurate classification effects, accurate connection strength, and classification powerful effect

Active Publication Date: 2020-01-14
YUNNAN POWER GRID CO LTD ELECTRIC POWER RES INST
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  • Claims
  • Application Information

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Problems solved by technology

The hyperspectral image semi-supervised classification method based on the forced k-nearest neighbor graph is very sensitive to the graph structure. The similarity between samples and the graph sparse method will greatly ch

Method used

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  • Image semi-supervised classification method and device and computer readable storage medium
  • Image semi-supervised classification method and device and computer readable storage medium
  • Image semi-supervised classification method and device and computer readable storage medium

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Embodiment 1

[0067] figure 1 A flow chart of an image semi-supervised classification method according to an embodiment of the present application is shown.

[0068] The embodiment of the present application provides a hyperspectral image semi-supervised classification method based on neighbor attribute complementarity, the process of the method is as follows figure 1 shown, including the following steps:

[0069] In step S1, read in hyperspectral cube data: H(x,y,z) and training set D L ={V L ,Y L},,

[0070] Among them, x and y represent the spatial pixel position, z represents the spectral band position, and V L is the set of training pixels, Y L is the corresponding training label set.

[0071] Hyperspectral images have a large number of bands and many types of ground objects, so the spectral characteristics of pixels are diverse. In addition, factors such as low spatial resolution, heterogeneity of ground object distribution, and multiple scattering effects will aggravate the d...

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Abstract

The invention relates to the technical field of high-dimensional image processing, in particular to an image semi-supervised classification method and device and a computer readable storage medium. The invention provides an image semi-supervised classification method. The method comprises the following steps: reading hyperspectral image cube data and a training set; rearranging the hyperspectral image cube data into a data matrix; calculating a full connection weight matrix; calculating k neighbors; forcing a sparse connection matrix of the nearest neighbor attribute; constructing a sparse connection matrix of mutual k neighbor attributes; obtaining a sparse connection matrix with complementary neighbor attributes; sparsifying the weight matrix by using a sparse connection matrix with complementary neighbor attributes to obtain a complementary neighbor sparse weight matrix; constructing an initial label matrix; and implementing semi-supervised image classification to obtain a classification result of all pixels of the hyperspectral image cube data.

Description

technical field [0001] The present application relates to the technical field of high-dimensional image processing, in particular, to an image semi-supervised classification method, device and computer-readable storage medium. Background technique [0002] A hyperspectral image is an image cube with high spectral dimensions acquired by a hyperspectral imager. Its spatial plane contains a large number of pixels representing ground object pixels, and each pixel is composed of an approximately continuous spectral feature. The spectral feature is essentially the reflectance of the feature object to light of different wavelengths, and reflects the specific material and properties of the feature, so it can be used to determine the category of the feature corresponding to the pixel. The large amount of data and high-dimensional features of hyperspectral images have brought great challenges to image processing and classification tasks. In addition, since the spectral characteristic...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/24147Y02A40/10
Inventor 周仿荣赵现平马仪沈志金晶郭晨鋆彭晶
Owner YUNNAN POWER GRID CO LTD ELECTRIC POWER RES INST
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