Remote sensing image small sample target detection method based on prototype convolutional neural network

A convolutional neural network, target detection technology, applied in remote sensing image small sample target detection, remote sensing image target detection field, can solve difficult remote sensing images and other problems, achieve high detection accuracy, save memory, and good robustness.

Pending Publication Date: 2021-05-28
NORTHWESTERN POLYTECHNICAL UNIV
View PDF0 Cites 25 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The existing small sample target detection methods are difficult to be directly applied to remote sensing images

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
  • Remote sensing image small sample target detection method based on prototype convolutional neural network
  • Remote sensing image small sample target detection method based on prototype convolutional neural network
  • Remote sensing image small sample target detection method based on prototype convolutional neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0022] The present invention will be further described below in conjunction with the accompanying drawings and embodiments, and the present invention includes but not limited to the following embodiments.

[0023]The hardware environment used for implementation is: Intel(R) Core(TM) i3-8100 CPU computer, 8.0GB memory, and the running software environment is: Ubuntu16.04.5LTS and Pycharm2018. This experiment uses the large-scale remote sensing image public database DIOR Dataset, which has a total of 23463 images, of which 5862 images are divided into the training set, 5863 images are divided into the verification set, and the remaining 11738 images are divided into the test set. Covers 20 common remote sensing image targets. Each category contains approximately 1200 images with an image size of 800 × 800 pixels and a spatial resolution ranging from 0.5 m / pixel to 30 m / pixel. In order to verify the effectiveness of the proposed scheme above, baseball fields, basketball courts, ...

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 a remote sensing image small sample target detection method based on a prototype convolutional neural network. A target detection network mainly comprising a feature extraction and category prototype acquisition module, a prototype guidance RPN module, a redirection feature map module and a detector module is constructed, network model basic learning is performed on a base class data set containing a large number of labeled samples, then a network model is finely adjusted on a balanced sub-data set, and finally, post-processing operations such as non-maximum suppression and the like are performed to realize multi-class target detection of the small sample remote sensing image. According to the method, different types of targets can be rapidly and accurately detected from the optical remote sensing image with the complex background by using a small amount of new class annotation data, and the method has relatively high detection precision and relatively high detection speed.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and in particular relates to a small sample object detection method of remote sensing images based on a prototype convolutional neural network, which can be applied to remote sensing image object detection in the case of small samples with very few labeled sample data. Background technique [0002] With the continuous development of satellite remote sensing technology, the acquisition of massive remote sensing images has become easier and easier. However, the labeling of remote sensing images requires a lot of manpower and financial resources. In addition, some categories of objects are relatively rare, and there is a problem that data is difficult to obtain. Therefore, how to use a small number of labeled samples to realize target detection in remote sensing images has become one of the problems to be solved urgently. [0003] The existing small sample target detection ...

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): G06K9/00G06K9/32G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/04G06N3/08G06V20/13G06V10/25G06V10/462G06V2201/07G06F18/24
Inventor 程塨施佩珍闫博唯姚西文韩军伟郭雷
Owner NORTHWESTERN POLYTECHNICAL 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