Hyperspectral remote sensing surface feature classification method based on superpixel-tensor sparse coding

A technology for hyperspectral remote sensing and ground object classification, which is applied in the field of hyperspectral image classification and image processing, and can solve problems that affect the accuracy of hyperspectral image recognition, do not consider spatial information, and have poor spatial consistency in homogeneous regions.

Active Publication Date: 2015-11-18
XIDIAN UNIV
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Problems solved by technology

The disadvantage of this method is that in the process of solving the sparse coefficient, it is necessary to solve the sparse coefficient on the mapped dictionary for each mapped sample. would create a large computational burden
The disa

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  • Hyperspectral remote sensing surface feature classification method based on superpixel-tensor sparse coding
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  • Hyperspectral remote sensing surface feature classification method based on superpixel-tensor sparse coding

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[0060] The present invention will be further described below in conjunction with the accompanying drawings.

[0061] Refer to attached figure 1 , the implementation steps of the present invention are as follows:

[0062] Step 1, input the hyperspectral image to be classified.

[0063] Input the hyperspectral image to be classified, and set each pixel in the input hyperspectral image as a sample.

[0064] Step 2, construct the hierarchical spatial similarity matrix.

[0065] In the first step, select the spatial neighbor sample around any sample in the hyperspectral image to be classified, and according to the distance between the sample and its spatial neighbor sample, take the spatial neighbor sample closest to the sample as the first layer of spatial neighbor sample, and set the distance The spatial neighbor samples closer to the sample are taken as the second-level spatial neighbor samples, and the spatial neighbor samples farthest from the sample are taken as the third-...

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Abstract

The invention discloses a hyperspectral remote sensing surface feature classification method based on superpixel-tensor sparse coding, and irons out the defects that the prior art cannot makes the most of the spatial information of a hyperspectral image to carry out classification and is low in classification speed. The method comprises the steps: (1) inputting a to-be-classified hyperspectral image; (2) building a hierarchical spatial similarity matrix; (3) obtaining a superpixel set; (4) building a mark sample dictionary; (5) solving a sparse coefficient matrix; (6) classifying superpixels; (7) outputting the classification results of the to-be-classified hyperspectral image. The method is advantageous in maintaining the spatial consistency of homogeneous regions of the hyperspectral image and being high in classification speed, and can be used for the rapid classification of the hyperspectral image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a hyperspectral remote sensing object classification method based on superpixel tensor sparse coding in the technical field of hyperspectral image classification. The invention can be used for fast ground object classification on hyperspectral remote sensing images. Background technique [0002] At present, the focus of research on hyperspectral data is mainly two aspects of dimensionality reduction and classification. Among them, the classification is mainly to process the data according to the different characteristic information of different types of data, and assign a label to each pixel, so as to realize the classification and recognition of the ground objects. According to whether there are labeled samples to participate in the processing process, classification methods can be divided into unsupervised classification methods, supervised classification method...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/24133
Inventor 杨淑媛李素婧王敏刘志周红静冯志玺刘红英马晶晶马文萍侯彪
Owner XIDIAN UNIV
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