A multi-kernel learning method for hyperspectral image classification that maximizes class separability

A hyperspectral image and multi-core learning technology, which is applied in character and pattern recognition, instruments, calculations, etc., can solve problems such as low solution efficiency and does not consider multi-core model solving and subsequent classification applications, so as to achieve high algorithm efficiency and improve algorithm operations Efficiency, the effect of improving classification performance

Active Publication Date: 2020-04-24
HARBIN INST OF TECH
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Problems solved by technology

[0004] In order to solve the problem that the existing multi-kernel learning method applied to hyperspectral image classification does not consider the combination of multi-kernel model solving and subsequent classification applications and the problem of low solution efficiency, the present invention proposes a criterion based on maximizing category separability multi-core learning method

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  • A multi-kernel learning method for hyperspectral image classification that maximizes class separability
  • A multi-kernel learning method for hyperspectral image classification that maximizes class separability
  • A multi-kernel learning method for hyperspectral image classification that maximizes class separability

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specific Embodiment approach 1

[0017] Specific implementation mode 1. Combination figure 1 To illustrate this embodiment, a multi-core learning method based on maximizing category separability criterion described in this embodiment is performed in the following steps:

[0018] Step 1. Obtain training samples and test samples from a given input hyperspectral image dataset;

[0019] Step 2. Use the training sample set Construct the base kernel matrix K in the multi-kernel learning model m , get the base kernel matrix set

[0020] Step 3, using the base kernel matrix set to measure the intra-class dispersion and inter-class dispersion of the hyperspectral image dataset in the Hilbert kernel space;

[0021] Step 4: On the basis of intra-class dispersion and inter-class dispersion, class separability is measured with the maximum class interval criterion, and the maximum class separability is used as the solution criterion for the multi-kernel learning model to solve the base kernel weight.

[0022] This e...

specific Embodiment approach 2

[0025] Specific embodiment 2: The difference between this embodiment and specific embodiment 1 is that in the step 1, training samples and test samples are obtained from a given input hyperspectral image data set; the specific process is:

[0026] Step 11. The given input hyperspectral image dataset is: x i ∈ R F

[0027] Among them, T represents the total number of samples in the input hyperspectral image dataset, Input hyperspectral image dataset X input Contains samples of class C, and the number of samples of class l is N l ;1≤l≤C;x i For the i-th sample in the input hyperspectral image dataset, R F is a space representing dimension F, F is the feature dimension of the input hyperspectral image data set, and i is a positive integer;

[0028] Step 12. From the input hyperspectral image dataset X input Randomly select samples according to p% of each class to form a training sample set:

[0029] Among them, p is determined by the complexity of the input data and ...

specific Embodiment approach 3

[0031] Specific embodiment three: the difference between this embodiment and specific embodiment one or two is that the training sample set is used in the second step Construct the base kernel matrix K in the multi-kernel learning model m , get the base kernel matrix set The base kernel matrix is ​​constructed as follows:

[0032] The training sample set The middle samples are input in pairs to the Gaussian kernel function to calculate the base kernel matrix where σ m is the Gaussian kernel scale parameter, x j is the jth sample in the training sample set, and i and j are positive integers.

[0033] Other steps and parameters are the same as those in Embodiment 1 or Embodiment 2.

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Abstract

A hyperspectral image classification multi-kernel learning method that maximizes category separability, and the invention relates to solving multi-kernel learning models. The present invention aims to solve the problems that the existing multi-kernel learning method applied to hyperspectral image classification does not consider the combination of multi-kernel model solution and subsequent classification application and the solution efficiency is low. Follow the steps below: step 1, obtain training samples and test samples from a given input hyperspectral image data set; step 2, use the training sample set to construct the base kernel matrix K in the multi-kernel learning model m , get the base kernel matrix set. Step 3: Use the base kernel matrix set to measure the intra-class dispersion and the inter-class dispersion of the data set in the Hilbert kernel space; Step 4. Based on the intra-class dispersion and the inter-class dispersion, use The maximum category interval criterion measures the category separability, and the maximum category separability is used as the solution criterion for the multi-kernel learning model to solve the base kernel weight. The invention is used in the field of pattern recognition.

Description

technical field [0001] The invention belongs to the field of pattern recognition and relates to solving multi-core learning models. Background technique [0002] Existing multi-kernel learning methods can be summarized into three categories. The first type is a multi-core learning method based on fixed criteria. This type of method uses fixed criteria such as addition and multiplication to combine base cores during the multi-core learning process, without considering the similarity measurement characteristics of each base core and without using any optimization criteria. Hence the classification performance is lower. The second type is the single-step optimization multi-core learning method. This type of method solves the weight parameters of the base core in the multi-core model and the parameters in the classifier at the same time, so the calculation amount is very large, the learning efficiency is very low, and its classification performance is relatively strong. The th...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06K9/46
CPCG06V10/462G06F18/2411
Inventor 谷延锋王青旺
Owner HARBIN INST OF TECH
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