Unlock instant, AI-driven research and patent intelligence for your innovation.

A training method of convolutional neural network for sar image ship classification and its classification method, ship classification model

A convolutional neural network and training method technology, applied in biological neural network models, neural architectures, character and pattern recognition, etc., can solve problems such as a large amount of training data, time-wasting labeled data, and difficulty in obtaining

Active Publication Date: 2021-12-28
AEROSPACE INFORMATION RES INST CAS
View PDF5 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the bottleneck of using a deep learning model is that it requires a large amount of training data, and obtaining a large amount of labeled data is time-consuming and difficult to obtain

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
  • A training method of convolutional neural network for sar image ship classification and its classification method, ship classification model
  • A training method of convolutional neural network for sar image ship classification and its classification method, ship classification model
  • A training method of convolutional neural network for sar image ship classification and its classification method, ship classification model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0040] Below, specific embodiments of the present invention will be described in detail in conjunction with the accompanying drawings, but they are not intended to limit the present invention.

[0041] It should be understood that various modifications may be made to the embodiments disclosed herein. Accordingly, the above description should not be viewed as limiting, but only as exemplifications of embodiments. Those skilled in the art will envision other modifications within the scope and spirit of the disclosure.

[0042] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and, together with the general description of the disclosure given above and the detailed description of the embodiments given below, serve to explain the embodiments of the disclosure. principle.

[0043] These and other characteristics of the invention will become apparent from the following description of preferred...

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 training method and a classification method of a convolutional neural network for ship classification in SAR images. The training method includes: obtaining slices with ship categories in SAR images; The convolutional neural network for image ship classification is trained so that it can reach the preset training accuracy; wherein, the convolutional neural network for SAR image ship classification is based on the first model, removing the first model of the first model A fully connected layer is constructed by adding a second fully connected layer according to the number of ship categories of the slices with the ship category. The training method can realize the training of the convolutional neural network used for SAR image ship classification in the case of only a small amount of training data, so that it can reach the preset training accuracy. And the convolutional neural network for SAR image ship classification that has reached the preset training accuracy in the present invention is applied to SAR image ship classification, and its ship classification accuracy can reach 97.54%.

Description

technical field [0001] The present application relates to the field of remote sensing, in particular to a training method and a classification method of a convolutional neural network for ship classification in SAR images. Background technique [0002] Since 2007, several high-resolution SAR satellites have been successfully launched, such as Cosmo-SkyMed, TerraSAR-X, ALOS-2PALSAR-2, Gaofen-3, etc., based on the resolution of high-resolution satellite SAR images obtained by satellites More than 3 meters, it contains a wealth of information on ground features, such as the geometric characteristics of ships, making it possible to distinguish different types of ships. [0003] The deep learning model (for example, convolutional neural network) can automatically learn the information representing the features in the SAR image, and provides the corresponding end-to-end processing without manual intervention, thus saving feature extraction and selection and optimal classification....

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 Patents(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06V20/13G06N3/045G06F18/24G06F18/214
Inventor 王超王原原张红
Owner AEROSPACE INFORMATION RES INST CAS