Hyperspectral image classification method and system

A technology of hyperspectral images and classification methods, applied in neural learning methods, instruments, scene recognition, etc., can solve the problem that the amount of model parameters and running time affect the efficiency of model task processing, the difficulties and limitations of hyperspectral image acquisition and data labeling Convolutional layer stacking depth and other issues, to achieve the effect of improving model performance and generalization ability, expanding receptive field, and reducing model calculation amount

Pending Publication Date: 2022-06-03
山东锋士信息技术有限公司
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  • Abstract
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  • Claims
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AI Technical Summary

Problems solved by technology

[0006] However, due to the dual difficulties of image acquisition and data labeling, hyperspectral images are inherently accompanied by a small sample problem, which limits the stacking depth of convolutional layers for models dealing with hyperspectral image classification tasks
In addition, the deeper convolution model will also affect the efficiency of model task processing in terms of model parameters and running time

Method used

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  • Hyperspectral image classification method and system
  • Hyperspectral image classification method and system
  • Hyperspectral image classification method and system

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Embodiment 1

[0045] This embodiment provides a hyperspectral image classification method, such as figure 1 As shown, it specifically includes the following steps:

[0046] Step 1. Obtain a hyperspectral image and perform preprocessing on the hyperspectral image.

[0047] Specifically, the specific method of preprocessing is: performing mean-variance standardization processing on each spectral channel in the acquired hyperspectral image, so as to accelerate the convergence speed of the proposed network model during the training process.

[0048] Step 2. Input the preprocessed hyperspectral image into the hyperspectral image classification network to obtain the category of each pixel in the acquired hyperspectral image. like figure 1 As shown, the hyperspectral image classification network includes a compression dilation block (including a compression dilation module), a self-attention block (a self-attention module) and a classifier.

[0049] For the image block input by the network mode...

Embodiment 2

[0065] This embodiment provides a hyperspectral image classification system, which specifically includes the following modules:

[0066] A preprocessing module configured to: acquire a hyperspectral image, and preprocess the hyperspectral image;

[0067] The compression and expansion module is configured to: perform channel interaction and compression of the spectral dimension of the pixel on the preprocessed hyperspectral image, and expand and align the spatial window of the spatial dimension of the pixel sample to obtain the mapping feature;

[0068] The self-attention module is configured to obtain spectral-spatial features after performing two information interactions in sequence based on the mapping features;

[0069] A classification module configured to: obtain the category of each pixel in the acquired hyperspectral image by using a classifier based on the spectral-spatial feature;

[0070] Among them, information interaction is to sequentially perform spectral featur...

Embodiment 3

[0073] This embodiment provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps in the hyperspectral image classification method described in the first embodiment above are implemented.

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Abstract

The invention provides a hyperspectral image classification method and system, and the method comprises the steps: obtaining a hyperspectral image, and carrying out the preprocessing of the hyperspectral image; performing channel interaction and compression of a pixel spectral dimension and space window expansion and alignment of a pixel sample space dimension on the preprocessed hyperspectral image to obtain mapping features; based on the mapping features, performing information interaction twice in sequence to obtain spectrum-space features; based on the spectrum-space features, a classifier is adopted to obtain the category of each pixel in the obtained hyperspectral image; according to the information interaction, after spectral feature channel compression, non-local space information extraction and spectral feature channel expansion are sequentially carried out on input features, the input features are fused with the input features, and output features are obtained. A receptive field for capturing spatial information is expanded, and richer and more robust spectral spatial information can be captured to efficiently complete a hyperspectral image classification task.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a hyperspectral image classification method and system. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] Hyperspectral image classification is the core technology of hyperspectral image processing and interpretation. According to a given set of object categories, based on the spectral and spatial characteristics of hyperspectral images, the task of hyperspectral image classification aims to classify each hyperspectral image pixel Assign unique semantic labels. [0004] Traditional hyperspectral image classification methods are difficult to efficiently extract discriminative and robust spectral-spatial features from the perspective of spectral and spatial dimensions to complete the task of pixel label assignment, and in complex scenes, ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06V20/13G06V10/77G06V10/58G06V10/764G06V10/80G06V10/82G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06N3/047G06N3/045G06F18/213G06F18/2414G06F18/253
Inventor 孙启玉杨公平刘玉峰孙平褚德峰
Owner 山东锋士信息技术有限公司
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