Hyperspectral remote sensing data classification method based on deep learning

A hyperspectral remote sensing and deep learning technology, applied in the field of hyperspectral data classification, can solve the problem of low classification accuracy, achieve accurate data classification and reduce the effect of impact

Active Publication Date: 2014-10-15
HARBIN INST OF TECH
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Problems solved by technology

[0004] The purpose of the present invention is to solve the problem of low classification accuracy in existing methods for classifying hyperspectral data containing nonlinear features, and to provide a method for classifying hyperspectral remote sensing data based on deep learning

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  • Hyperspectral remote sensing data classification method based on deep learning

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specific Embodiment approach 1

[0033] Specific implementation mode one: the following combination figure 1 Describe this embodiment, the hyperspectral remote sensing data classification method based on deep learning described in this embodiment, it comprises the following steps:

[0034] Step 1: Read the hyperspectral raw data, use the principal component analysis method to obtain the eigenvalues ​​and eigenvectors of the hyperspectral raw data, and then obtain the spectral eigenvectors of the hyperspectral raw data; Domain information extraction to obtain spatial feature information of hyperspectral raw data;

[0035] Step 2: Integrate the spectral feature vector and spatial feature information of the hyperspectral raw data to obtain hyperspectral integrated data;

[0036] Step 3: Determine the labeled samples from the hyperspectral integrated data, and select training samples and test samples from the labeled samples;

[0037] Step 4: Based on the deep learning method, use the training samples to pre-tr...

specific Embodiment approach 2

[0040] Specific implementation mode 2: This implementation mode further explains the implementation mode 1, and the acquisition method of the spectral feature vector of the hyperspectral raw data is as follows:

[0041] First calculate the covariance matrix of the hyperspectral raw data, and use the principal component analysis method to calculate the eigenvalues ​​and eigenvectors of all the hyperspectral raw data according to the covariance matrix, and arrange the corresponding features according to the order of the eigenvalues ​​from large to small Vector; use eigenvectors as weighting coefficients to calculate and obtain B band principal component components of all eigenvectors, and use all eigenvectors containing B band principal component components as spectral eigenvectors of hyperspectral raw data; B is a positive integer;

[0042] The method to obtain the spatial feature information of hyperspectral raw data is:

[0043] Select the first N principal component componen...

specific Embodiment approach 3

[0047] Specific implementation mode three: this implementation mode further explains implementation mode two, and the specific method for obtaining hyperspectral integrated data is: the pixel point (x i ,y i ) is regarded as a spectral feature vector of length B, and all pixels at the same coordinate position in the hyperspectral raw data (x i ,y i ), a spectral eigenvector of length B and a length of N×w 2 The spatial feature information of is integrated into a length of (B+N×w 2 ) vector, as hyperspectral integration data.

[0048] In this embodiment, after information integration of hyperspectral data, each pixel will have a length (B+N×w) containing spectral information and spatial information 2 ) eigenvectors.

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Abstract

The invention discloses a hyperspectral remote sensing data classification method based on deep learning, and belongs to the technical field of hyperspectral data classification. The invention aims to solve a problem of low classification precision of a method for classifying hyperspectral remote sensing data with nonlinear characteristics. The hyperspectral remote sensing data classification method comprises the following steps: firstly, processing hyperspectral original data to obtain the spectral feature vector and the spatial feature information of the hyperspectral original data; then, integrating the spectral feature vector with the spatial feature information; confirming labeled samples by hyperspectral integrated data, selecting a training sample and a test sample from the labeled samples; Pre-training a multi-layer restricted Boltzmann machine which forms a deep network by the training sample; carrying out supervised learning to the network formed by the multi-layer restricted Boltzmann machine through the training sample; and inputting the test sample into the trimmed network formed by the multi-layer restricted Boltzmann machine to realize hyperspectral remote sensing data classification. The invention is used for the hyperspectral remote sensing data classification.

Description

technical field [0001] The invention relates to a hyperspectral remote sensing data classification method based on deep learning, and belongs to the technical field of hyperspectral data classification. Background technique [0002] With the successful development of airborne and spaceborne hyperspectral sensors, the spectral resolution of remote sensing data has been greatly improved, which solves the problems that cannot be solved by multispectral remote sensing. has become an urgent problem to be solved. The high dimension of the data comes from the high spectral resolution. For hyperspectral remote sensing with a spectral resolution of nanometers, the data dimension of the spectrum can reach hundreds of dimensions. The high-dimensional space of the data is greatly restricted. [0003] At present, the mainstream method to solve the high-dimensional problem of hyperspectral data is feature extraction. However, for the nonlinear features contained in hyperspectral data, ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/66
Inventor 陈雨时赵兴王强时春雨
Owner HARBIN INST OF TECH
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