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A hyperspectral image classification algorithm based on 3D convolution spectral space feature fusion

A hyperspectral image and feature fusion technology, applied in the field of hyperspectral image classification algorithms based on 3D convolutional spectral-space feature fusion, can solve problems such as inability to effectively deal with image noise, unsatisfactory accuracy, and incomplete information, and achieve classification accuracy The effect of promoting and improving training speed and high computing efficiency

Active Publication Date: 2019-05-03
LIAONING TECHNICAL UNIVERSITY
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Problems solved by technology

[0002] In the field of hyperspectral image classification, support vector machine (SVM) is considered the most popular method due to its high efficiency. It is a supervised learning model that maps the sample space to a high-dimensional In the feature space, the nonlinear separable problem in the original sample space is transformed into a linear separable problem in the feature space, so as to realize the classification of hyperspectral images. However, this method has low classification accuracy and cannot effectively deal with image The existing noise and the accuracy in the small sample environment cannot meet the actual application requirements; therefore, in recent years, a hyperspectral image spectral-space feature fusion method using the principal component analysis method to reduce the dimensionality and using Gabor features has emerged to improve the classification accuracy , although this method can improve the classification accuracy of hyperspectral images to a certain extent, it often sacrifices part of the information carried in the data, making the information after spectral-space fusion incomplete, which limits the upper limit of its performance.

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  • A hyperspectral image classification algorithm based on 3D convolution spectral space feature fusion
  • A hyperspectral image classification algorithm based on 3D convolution spectral space feature fusion
  • A hyperspectral image classification algorithm based on 3D convolution spectral space feature fusion

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Embodiment

[0033] Example: such as figure 1 As shown, a hyperspectral image classification algorithm based on 3D convolutional spectral-spatial feature fusion, the algorithm is a four-layer structure, the first layer is to use 3D convolutional neural network to achieve spectral-spatial feature fusion, and its input data is A1 , the output is A2, the second and third layers use 1D convolutional neural network to extract features from the data, the input and output of the second layer are A2 and A3 respectively, and the input and output of the third layer are A3 and A3 respectively A4, the fourth layer is fully connected to the third layer, and the input and output are A4 and A5 respectively; it is characterized in that the hyperspectral image classification algorithm includes the following steps:

[0034] Step 1: Preprocess the data; use PCA whitening as a data preprocessing method to reduce the correlation of spectral information between different spectral channels, and obtain the data a...

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Abstract

The invention discloses a hyperspectral image classification algorithm based on 3D convolution spectral space feature fusion, and the algorithm reduces the correlation between spectral features through employing PCA whitening, and greatly improves the training speed of a neural network. According to the method, the data is subjected to feature extraction in a manner of combining 3D convolution operation and 1D convolution operation; compared with the prior art, the method has the advantages that the classification precision can be higher, the representation effect on small samples is better, the calculation efficiency is higher, no information loss exists in the feature fusion process, the efficient and accurate classification can be completed, and in addition, the classification precisionis obviously promoted by using the 3D convolution hyperspectral image spectral space feature fusion method.

Description

technical field [0001] The invention relates to the technical field of hyperspectral image classification, in particular to a hyperspectral image classification algorithm based on 3D convolution spectral-space feature fusion. Background technique [0002] In the field of hyperspectral image classification, support vector machine (SVM) is considered the most popular method due to its high efficiency. It is a supervised learning model that maps the sample space to a high-dimensional In the feature space, the nonlinear separable problem in the original sample space is transformed into a linear separable problem in the feature space, so as to realize the classification of hyperspectral images. However, this method has low classification accuracy and cannot effectively deal with image The existing noise and the accuracy in the small sample environment cannot meet the actual application requirements; therefore, in recent years, a hyperspectral image spectral-space feature fusion m...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCY02A40/10
Inventor 张海涛孟令国
Owner LIAONING TECHNICAL UNIVERSITY
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