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Remote sensing scene image classification method based on channel multi-group fusion

A scene image and classification method technology, applied in the field of remote sensing scene image classification, can solve the problems of single grouping form of channel grouping and low extraction accuracy

Pending Publication Date: 2022-02-01
QIQIHAR UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that the channel grouping in the existing channel fusion uses a single grouping form, resulting in low accuracy of feature extraction, and proposes a remote sensing scene image classification method based on channel multi-group fusion

Method used

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  • Remote sensing scene image classification method based on channel multi-group fusion
  • Remote sensing scene image classification method based on channel multi-group fusion
  • Remote sensing scene image classification method based on channel multi-group fusion

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specific Embodiment approach 1

[0049] Specific implementation mode 1: The specific process of the remote sensing scene image classification method based on channel multi-group fusion in this implementation mode is as follows:

[0050] Step 1, acquiring hyperspectral images (labeled);

[0051] Step 2. Establishing a lightweight convolution neural network based on channel multigroup fusion (LCNN-CMGF) model;

[0052] Step 3: Input the hyperspectral image (labeled) into the established lightweight convolution neural network based on channel multigroup fusion (LCNN-CMGF) model for training, and obtain the trained A lightweight convolution neural network based on channel multigroupfusion (LCNN-CMGF) model based on channel multigroup fusion;

[0053] The lightweight convolutional neural network model based on channel multi-packet fusion includes input layer, first group (Group1), second group (Group2), third group (Group3), fourth group (Group4), fifth group Group (Group5), sixth group (Group6), seventh group (...

specific Embodiment approach 2

[0083] Specific embodiment two: the difference between this embodiment and specific embodiment one is that the

[0084] The first three groups are used to extract shallow information of remote sensing images;

[0085] Among them, the first and second groups adopt the proposed three-branch shallow downsampling structure;

[0086] The first group and the second group respectively include branch 1, branch 2 and branch 3;

[0087] Branch 1 sequentially includes the convolution layer with the first step size of 2 convolution kernel size 3×3, batch normalization, Rule activation function, the second step size of 1 convolution layer with convolution kernel size of 3×3, batch Standardization, Rule activation function;

[0088] Branch 2 sequentially includes a maximum pooling layer with a step size of 2 and a pooling kernel size of 2, and a third convolution layer with a step size of 1 and a convolution kernel size of 3×3;

[0089] Branch 3 includes a convolutional layer with a four...

specific Embodiment approach 3

[0111] Specific implementation mode 3: The difference between this implementation mode and specific implementation mode 1 or 2 is that the third group sequentially includes a convolution layer with a fifth step size of 1 and a convolution kernel size of 1×1, and a sixth step size of 1 Convolution layer with a convolution kernel size of 3×3 and a depth-separable convolution layer with a convolution kernel size of 2×3 in the first step; the specific process is:

[0112] The third group uses a hybrid convolution method combining standard convolution and depthwise separable convolution to extract features.

[0113] Compared with standard convolution, depthwise separable convolution has a large reduction in the number of parameters.

[0114] Suppose the input feature size is H×W×C 1 , the convolution kernel size is H 1 ×W 1 ×C 1 , the output feature size is H×W×C 2 , then the parameter volume of the standard convolutional layer convolution is:

[0115] params conv =(H 1 ×W ...

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Abstract

The invention discloses a remote sensing scene image classification method based on channel multi-group fusion, and relates to a remote sensing scene image classification method. The objective of the invention is to solve the problem of low feature extraction accuracy due to the fact that channel grouping in existing channel fusion uses a single grouping form. The method comprises the following steps: step 1, acquiring a hyperspectral image; step 2, establishing a lightweight convolutional neural network model based on channel multi-group fusion; step 3, inputting the hyperspectral image into the established lightweight convolutional neural network model based on channel multi-group fusion for training to obtain a trained lightweight convolutional neural network model based on channel multi-group fusion; and step 4, inputting a to-be-measured hyperspectral image into the trained lightweight convolutional neural network model based on channel multi-group fusion to obtain a classification result. The method is applied to the field of remote sensing scene image classification.

Description

technical field [0001] The invention relates to a remote sensing scene image classification method. Background technique [0002] The goal of remote sensing scene image classification is to correctly classify the input remote sensing images. Remote sensing image classification has attracted attention due to its wide application in natural disaster detection, land cover analysis, urban planning and national defense security [1,2,3,4]. So far, many methods have been proposed for remote sensing scene image classification. Among them, the convolutional neural network has become the most successful deep learning method recognized by its own powerful feature extraction ability, and is widely used in image classification [5], target detection [6] and other directions. Many excellent neural networks have been designed for image classification. For example, Li et al. [7] proposed a deep feature fusion network for remote sensing scene classification. Zhao et al. [8] proposed a PTM...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06V10/764G06V10/774G06V10/80G06V30/19G06K9/62G06V20/10
CPCG06F18/241G06F18/253G06F18/214
Inventor 石翠萍张鑫磊王丽婧
Owner QIQIHAR UNIVERSITY