SAR (synthetic aperture radar) image change detection method based on non-supervision depth nerve network

A deep neural network, image change detection technology, applied in the field of deep learning and remote sensing image processing

Inactive Publication Date: 2014-05-21
XIDIAN UNIV
View PDF3 Cites 68 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

But this method also introduces a new problem, that is, the construction of the difference map

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
  • SAR (synthetic aperture radar) image change detection method based on non-supervision depth nerve network
  • SAR (synthetic aperture radar) image change detection method based on non-supervision depth nerve network
  • SAR (synthetic aperture radar) image change detection method based on non-supervision depth nerve network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0060] The present invention proposes a SAR image change detection algorithm based on unsupervised deep network learning, which belongs to the technical field of combining neural network and image processing, and mainly solves the problem that the SAR image change detection process is not solved directly by solving the difference map. The problem of finding the changing area of ​​two images. Its characteristics are: (1) Firstly, FCM joint classification is performed on two registered SAR images of the same area in different phases to obtain rough change detection results; Noise points are used as samples for deep network training; (3) input the sample points to be trained into the designed deep neural network for training; (4) input two images to be detected into the trained deep network, and obtain The final change detection result plot.

[0061] Such as figure 1 shown.

[0062] The main flowchart step features are:

[0063] Step 101: start the SAR image change detection ...

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 SAR (synthetic aperture radar) image change detection algorithm based on non-supervision depth network learning. The algorithm includes the steps: 101 starting an SAR image change detection method based on a non-supervision depth nerve network; 102 performing FCM (fuzzy c-mean) joint classification for two registered SAR images of different time phases in the same area to obtain rough change detection results; 103 selecting noiseless points with large possibility to serve as training samples of the depth network according to initial change detection results; 104 inputting sample points to be trained to the designed depth nerve network to be trained; 105 inputting two images to be detected to the trained depth nerve network to obtain a final change detection result map; 106 finishing the SAR image change detection method based on the non-supervision depth nerve network. Ohm= {ohm1 and ohm2}. Construction links of a difference map are avoided, sensitivity of noise is improved to a certain extent, and detection efficiency and detection accuracy are remarkably improved.

Description

technical field [0001] The invention belongs to the combination of deep learning and remote sensing image processing field, mainly solves the problem of change detection of remote sensing images, and specifically provides a SAR image change detection method (DN) based on an unsupervised deep neural network to realize the detection of SAR image changes. Background technique [0002] Since 1978, synthetic aperture radar (SAR) has revolutionized radar technology. It has the characteristics of high resolution, all-day and all-weather work, which is unmatched by visible light and infrared sensors. This technology has been widely used in industrial and agricultural production, scientific research and military fields. SAR image change detection is through the comparison and analysis of two SAR images of the same area in different periods, and according to the difference between the images, the required ground object or target change information is obtained. The technical demand fo...

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): G06T7/00G06N3/02
Inventor 公茂果焦李成赵姣姣马文萍马晶晶刘嘉雷雨李豪
Owner XIDIAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap