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

Satellite picture defect-based positioning, identifying and classifying method

A satellite image and classification method technology, applied in image analysis, character and pattern recognition, image enhancement, etc., can solve the problems of low accuracy and cumbersome process, and achieve the effect of improving speed and accuracy

Pending Publication Date: 2020-04-10
北京华恒盛世科技有限公司
View PDF5 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] At present, the screening and diagnosis of defects in satellite images mainly rely on manual sieving, and the defects such as noise points and noise lines unique to satellite images are inspected and compared one by one with the naked eye. The process is cumbersome and the accuracy is not high.

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
  • Satellite picture defect-based positioning, identifying and classifying method
  • Satellite picture defect-based positioning, identifying and classifying method
  • Satellite picture defect-based positioning, identifying and classifying method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0030] Prepare 1300 satellite images, of which 400 are noisy pictures, 400 are noisy pictures, 400 are normal pictures, and 100 are unknown pictures, a machine with GPU, the machine is installed with Centos7.3 system, Tensorflow1.11.0 , CUDA9.0.

[0031] according to figure 1 The flowchart of the present invention performs image processing.

[0032] First follow figure 2 The data preprocessing process shown is preprocessed;

[0033] Then follo...

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 satellite picture defect-based positioning, identifying and classifying method. The method comprises the following steps of: (1) a preprocessing step: enhancing the characteristics of noisy points and noisy lines of original pictures, and segmenting all the enhanced pictures into a training data set and a test data set; (2) a model training step: feeding the training dataset into a deep learning neural network, carrying out iterative training, evaluating the effect of the trained model by using the test data set to obtain an optimal model; and (3) a model deploying step: reasoning an updated image by using the optimal model, and realizing the positioning, identification and classification of defects in the new image in real time. According to the method, end-to-end learning is realized, image screening speed and precision are improved, and an automatic, efficient and accurate solution is provided for the problem of low production efficiency of manual image screening which puzzles people in the past.

Description

Technical field [0001] The invention relates to the technical field of artificial intelligence computer vision, in particular to a method for positioning, identifying and classifying defects based on satellite images. Background technique [0002] Image recognition and classification is the cornerstone of the intersection of image understanding and artificial intelligence, and it is also the most challenging task. According to the paper review "Deep Learning for Generic Object Detection: A Survey" published in 2018, deep neural network is the learning of feature expression Provide strong support, especially for image recognition and classification tasks, and are driving major national strategic policies related to national economy and people's livelihood such as robot vision, e-commerce, autonomous driving, human-computer interaction, content-based image retrieval, intelligent video surveillance and augmented reality Sinking and landing. [0003] At present, the screening and diag...

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
IPC IPC(8): G06T7/00G06K9/62
CPCG06T7/0002G06T2207/10032G06T2207/20081G06T2207/20084G06F18/241
Inventor 苏斌
Owner 北京华恒盛世科技有限公司
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