Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Image change detection method based on depth-separable convolution network

A technology of image change detection and convolutional network, which is applied in the field of image processing, can solve the problems of low detection accuracy and achieve the effects of reducing negative impact, improving flexibility, and improving accuracy

Active Publication Date: 2018-11-20
XIDIAN UNIV
View PDF4 Cites 24 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to address the shortcomings of the above-mentioned prior art, and propose an image change detection method based on a deep separable convolutional network, which is used to solve the technical problem of low detection accuracy existing in the existing image change detection method

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Image change detection method based on depth-separable convolution network
  • Image change detection method based on depth-separable convolution network
  • Image change detection method based on depth-separable convolution network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0055] Below in conjunction with accompanying drawing and specific embodiment, the present invention is described in further detail:

[0056] refer to figure 1 , an image change detection method based on a depthwise separable convolutional network, comprising the following steps:

[0057] Step 1 Construct training sample set, validation sample set and test sample set:

[0058] (1a) In this example, each set of image sample pairs in the existing sample set SZTAKI AirChange Benchmark set is normalized to obtain multiple sets of normalized image sample pairs. like t in sample pair 2 Time image samples are stacked up to t 1 On the time image sample, multiple two-channel image samples are obtained, and the normalization formula adopted is as follows:

[0059]

[0060]

[0061] Among them, I A and I B Represents image samples at different times in the same place, I A ' means by I A Normalized image samples, I B ' means by I B Normalized image samples;

[0062] (1b) ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention provides an image change detection method based on a depth-separable convolution network, which is used for solving the technical problem of low detection accuracy existing in the conventional image change detection methods. The implementation steps include: constructing a training sample set, a verifying sample set and a test sample set; using a variant U-Net of a full convolution network as a basic network to establish a depth-separable convolution network; constructing a loss function of the trained depth-separable convolution network; training, testing and verifying the depth-separable convolution network; and using the verified finally-trained depth-separable convolution network to perform test to obtain a change detection result graph. The invention has rich image feature semantics and structure information extracted by the depth-separable convolution network, has strong image expression ability and discrimination, can improve the change detection accuracy, and canbe used in the technical fields of land cover detection, disaster assessment, video monitoring and the like.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to an image change detection method, in particular to an image change detection method based on a depth separable convolutional network in the technical field of remote sensing image change detection, which can be used for land cover detection, disaster assessment, video Surveillance and other technical fields. Background technique [0002] Image change detection refers to the use of multi-temporal images covering the same surface area and other auxiliary data to determine and analyze surface changes. It uses the computer image processing system to identify and analyze the changes of objects or phenomena in different periods of time; it can determine the changes of objects or phenomena within a certain time interval, and provide qualitative and quantitative information on the spatial distribution of objects and their changes. According to the basic unit of image data process...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06N3/04
CPCG06T7/0002G06T2207/20084G06T2207/20081G06T2207/10004G06N3/045
Inventor 焦李成刘若辰张浪浪任蕊冯捷慕彩红李阳阳
Owner XIDIAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com