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Image change detection method based on CNN-CDCN

An image change detection and image technology, applied in the field of image change detection based on neural network

Pending Publication Date: 2021-02-26
无锡禹空间智能科技有限公司
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  • Abstract
  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

However, homologous change detection methods have many limitations in practical applications.

Method used

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  • Image change detection method based on CNN-CDCN
  • Image change detection method based on CNN-CDCN
  • Image change detection method based on CNN-CDCN

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Experimental program
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Embodiment Construction

[0015] combine figure 1 The neural network structure diagram and figure 2 The flowchart, the present invention is based on the image change detection method of CNN-CDCN, comprises the following steps:

[0016] Step 1: Construct a symmetrical bilaterally coupled deep convolutional neural network structure, each side consisting of multiple convolutional layers, pooling layers, and coupling layers. Network parameters include convolution kernel, activation bias, etc., which are obtained during the learning process through the backpropagation algorithm.

[0017] Step 2: The two images to be detected P 1 ,P 2 Input the two ends of the symmetrical neural network respectively, use different convolution kernels in the convolution layer to convolve the input image data to obtain many feature maps, and the pooling layer downsamples the feature maps to reduce the feature dimension and remove redundant information , to enhance the robustness of the network to displacement, rotation, e...

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Abstract

The invention discloses an image change detection method based on CNN-CDCN, and relates to the field of machine learning, and the method comprises the following steps: constructing a convolutional neural network structure, and defining a change detection problem; preprocessing data, removing noise of corresponding images, reducing feature dimensions through a convolution layer and a pooling layer,removing redundant information, performing multi-layer convolution and pooling to finally obtain feature vectors capable of representing input images, and converting the two input images into the same feature space through a coupling layer to enable feature representation of the two input images to be more consistent; and finally, learning network parameters by optimizing a target function. According to the invention, the convolutional neural network is utilized to extract the local features of the image, feature conversion is carried out, an unsupervised method is adopted, additional prior information is not needed, and the invention is an autonomous learning intelligent algorithm.

Description

technical field [0001] The invention relates to the fields of machine learning and image recognition, in particular to an image change detection method based on a neural network. Background technique [0002] In recent years, with the continuous development of artificial intelligence and the implementation of more application scenarios, neural network models have been widely used in machine learning problems such as classification and regression. Artificial Neural Networks (ANNs for short) is an algorithmic mathematical model that imitates the behavioral characteristics of animal neural networks and performs distributed parallel information processing. This kind of network depends on the complexity of the system, and achieves the purpose of processing information by adjusting the interconnection relationship between a large number of internal nodes. With the emergence of big data and the rapid development of computer computing power, Deep Neural Network (DNN) with a deeper ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/40G06N3/04
CPCG06V10/30G06N3/045G06F18/213G06F18/22
Inventor 王堃
Owner 无锡禹空间智能科技有限公司
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