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

Multiresolution CNN-based millimeter-wave radar cloud map segmentation method

A millimeter-wave radar, multi-resolution technology, applied in neural learning methods, image enhancement, image analysis, etc., can solve problems such as insufficient segmentation accuracy and complex pre-processing

Inactive Publication Date: 2016-11-16
BEIJING UNIV OF TECH
View PDF2 Cites 48 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] To sum up, the existing cloud image segmentation algorithms require complex pre-processing, manual extraction of features for segmentation or segmentation through threshold judgment, etc. These methods can only be used for small-scale cloud image processing, and the segmentation Accuracy is not high enough

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
  • Multiresolution CNN-based millimeter-wave radar cloud map segmentation method
  • Multiresolution CNN-based millimeter-wave radar cloud map segmentation method
  • Multiresolution CNN-based millimeter-wave radar cloud map segmentation method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0067] In order to achieve the above problems, the present invention provides a cloud image segmentation method based on multi-resolution CNN (MR-CNN). The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0068] 1. Image preprocessing

[0069] The picture preprocessing of the present invention mainly has two aspects. One is that we need to manually mark the groundtruth of the cloud image (because there is no public data set in the field of cloud image processing, so we need to generate the groundtruth by ourselves, the manual labeling here is different from the traditional manual extraction of features, only need to mark dozens of Frame, the testing stage just can be automatically divided) as the supervisory signal when training CNN network; The 2nd, consider the feature that the information of combining context is more conducive to learning cloud image, the present invention needs to cut out the image area of ​​differ...

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 discloses a multiresolution CNN-based millimeter-wave radar cloud map segmentation method, and belongs to the field of image segmentation. The method comprises the following steps of: obtaining a high-temporal-spatial resolution cloud evolution map of a horizontal / vertical structure by utilizing millimeter wave cloud radar, wherein the information of a context of the cloud map is fully utilized; learning local and global features of the cloud map through respectively inputting three image areas with different resolutions into three CNN networks configured with same parameters; classifying the learned features into cloud features and non-cloud features through classifiers of the neural networks, so as to realize the segmentation of the cloud map; and finally combining the segmentation results of the three networks so as to ensure that the segmentation correctness of the cloud map is up to 99.67%.

Description

technical field [0001] The invention belongs to the field of image segmentation, and specifically relates to biological feature extraction and classification. A cloud image segmentation method based on multi-resolution CNN (MR-CNN) is proposed by using millimeter-wave cloud radar to obtain cloud evolution maps with high spatio-temporal resolution in horizontal and vertical structures. Background technique [0002] Clouds are water droplets, supercooled water droplets, ice crystals, or their mixtures formed by condensed water vapor in the atmosphere, suspended in the air, and sometimes contain some larger raindrops, ice particles, and snow crystals. It is a common weather phenomenon. . Cloud observation is an important part of ground observation. Accurate cloud observation can provide varying degrees of help for weather forecasting, flight safety, and artificial rainfall enhancement operations. At present, the information of cloud observation data is obtained mainly through...

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): G06T7/00G06N3/08
CPCG06N3/08G06T2207/10044
Inventor 毋立芳贺娇瑜张加楠马玉琨张世杰
Owner BEIJING UNIV OF TECH
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