Hyperspectral image classification method based on sparse low-rank regression

A technology of hyperspectral image and classification method, applied in the field of hyperspectral image classification, can solve the problems of long processing time and low classification accuracy, and achieve the effect of reducing cost, improving classification accuracy and classification speed

Active Publication Date: 2015-09-23
XIDIAN UNIV
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Problems solved by technology

When the existing linear regression method is used for hyperspectral image cla...

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  • Hyperspectral image classification method based on sparse low-rank regression
  • Hyperspectral image classification method based on sparse low-rank regression
  • Hyperspectral image classification method based on sparse low-rank regression

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[0027] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0028] refer to figure 1 , the concrete steps of the present invention are as follows:

[0029] Step 1, input a hyperspectral image to be classified which contains k categories and d bands, and set each pixel of the hyperspectral image as a sample.

[0030] Step 2: Perform 5×5 mean filtering on the samples in the spectral domain of the hyperspectral image, that is, calculate the average value of each pixel and the pixels in the 24 neighborhoods around the pixel as the value at the pixel.

[0031] Step 3: Randomly select 5% of the samples in the labeled spectral vector of the filtered hyperspectral image as the training sample X, and use the remaining 95% of the samples as the testing sample Z of the hyperspectral image.

[0032] Step 4, obtain the low-rank projection matrix A and parameter matrix B according to the training sample X.

[0033] (4a) initiali...

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Abstract

The invention discloses a hyperspectral image classification method based on sparse low-rank regression, and mainly solves a problem that the processing speed of a hyperspectral image in the prior art is low. The method comprises the steps: (1) reading the hyperspectral image data, and carrying out the mean filtering of the hyperspectral image data; (2) determining a training sample and a testing sample in a spectrum component with a label in the hyperspectral image after filtering; (3) solving a low-rank projection matrix and a parameter matrix according to the training sample; (4) solving an embedded characteristic matrix of the training sample and an embedded characteristic matrix of the testing sample according to the low-rank projection matrix and the parameter matrix; (5) employing a linear supporting vector machine classifier to classify the embedded characteristic matrix of the training sample and the embedded characteristic matrix of the testing sample, and obtaining a classification image. The method is high in classification precision, is low in cost of high-dimensional data processing, and can be used for the discrimination of surface features of the hyperspectral image.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and in particular relates to a hyperspectral image classification method, which can be used to distinguish ground objects in hyperspectral images. Background technique [0002] Hyperspectral image object classification is the main content of remote sensing technology processing, based on the following: the same type of pixel has consistency in spectral characteristics and spatial characteristics, and different object types have obvious differences in spectral characteristics and spatial characteristics. Hyperspectral data is a high-dimensional feature space composed of a large number of bands. There is correlation and redundancy between the bands, and a large amount of calculation is required for processing. Moreover, hyperspectral data has a high dimensionality, and the Hughes phenomenon is prone to occur during classification, so reduce the high Spectral data dimensional...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2411
Inventor 焦李成马文萍张风刘芳侯彪王爽杨淑媛
Owner XIDIAN UNIV
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