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

Small sample remote sensing image classification method based on prototype correction

A technology of remote sensing images and classification methods, applied in the field of image processing, can solve the problems of over-fitting of deep network models and poor classification performance, and achieve the effects of reducing background irrelevant information noise, improving classification accuracy, and improving category representation capabilities.

Active Publication Date: 2021-08-06
NORTHWESTERN POLYTECHNICAL UNIV
View PDF8 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is that when the number of samples is insufficient, the deep network model is overfitted and the classification performance is poor

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
  • Small sample remote sensing image classification method based on prototype correction
  • Small sample remote sensing image classification method based on prototype correction

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0038] The method of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments of the present invention.

[0039] It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and examples.

[0040] It should be noted that the terminology used here is only for describing specific implementations, and is not intended to limit the exemplary implementations according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and / or combinations thereof. ...

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 small sample remote sensing image classification method based on prototype correction. The method comprises the following steps: step 1, setting an overall network framework of small sample remote sensing image classification; step 2, pre-training the feature extractor and the self-attention model; step 3, expanding a support set sample; step 4, performing prototype correction by the expanded support set; and step 5, predicting the query set sample by using the corrected and expanded support set prototype and the classifier to obtain a final classification result. Saliency features of the remote sensing image can be effectively extracted by using the self-attention model, and the influence of background irrelevant information noise can be reduced; by correcting the prototype features of each category of the support set, the category characterization capability of the features can be improved, so that the remote sensing image classification precision under the small sample condition is improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a small sample remote sensing image classification method based on prototype correction. Background technique [0002] In recent years, deep learning has made breakthroughs in image processing, computer vision and other fields, and has also promoted the development of remote sensing image classification technology. Traditional image classification algorithms have been difficult to meet the performance and intelligence requirements of image processing in practical applications. The deep learning algorithm independently realizes the analysis and processing of image features by imitating the brain's cognition, and has powerful feature learning and representation capabilities, and has become the mainstream method of current image classification. [0003] Currently, image classification methods usually rely on a large amount of labeled data and require a long tra...

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): G06K9/62G06K9/46G06K9/00G06N3/04
CPCG06V20/13G06V10/462G06N3/045G06F18/214G06F18/241
Inventor 耿杰曾庆捷蒋雯邓鑫洋
Owner NORTHWESTERN POLYTECHNICAL 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