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

High-resolution SAR image classification method based on depth ladder network

A classification method and high-resolution technology, applied in the field of image processing, can solve problems such as time-consuming and unreliable images, and achieve the effect of reducing training data, reducing test time, and improving classification speed and accuracy

Active Publication Date: 2017-09-05
XIDIAN UNIV
View PDF9 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, manual labeling is time-consuming, and the labeled images are not reliable due to poor knowledge of the target area

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
  • High-resolution SAR image classification method based on depth ladder network
  • High-resolution SAR image classification method based on depth ladder network
  • High-resolution SAR image classification method based on depth ladder network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0045] The present invention will be further described below in conjunction with the accompanying drawings.

[0046] refer to figure 1 , the specific implementation steps of the high-resolution SAR image classification method based on the depth ladder network of the present invention are as follows:

[0047] Step 1, input the high-resolution SAR image to be classified, and its artificially labeled image; construct the training data set D1 and the test data set D2 from the high-resolution SAR image and its labeled image, and select the German DLR for the high-resolution SAR image to be classified The horizontal polarization map of the X-band tri-polarization data acquired by the ESAR sensor in Traunstein County, the image resolution is 1 meter, and the image size is 4278×6187 pixels;

[0048] Specific steps are as follows:

[0049] (1a) First, three times downsampling is performed on the original image of the high-resolution SAR image, and then a block with a size of 21×21 pi...

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 high-resolution SAR image classification method based on a depth ladder network, mainly to solve the problem that the high-resolution SAR image has few data with class identifiers and a network can not be trained effectively. The method comprises steps: a to-be-classified high-resolution SAR image and identifier information thereof are inputted; a training data set D1 and a test data set D2 are constructed; features of the data sets D1 and D2 are normalized to obtain data sets D3 and D4; a classifier model based on the depth ladder network is constructed; the training data set D3 is used for training the network; and the well-trained classifier model is used for classifying the test data set D4. The few training samples with class identifiers can be made full use of, and the high classification precision can be acquired.

Description

[0001] 【Technical field】 [0002] The invention belongs to the technical field of image processing, and in particular relates to a high-resolution SAR image classification method based on a deep ladder network, which can be used in target detection and ground object classification methods. [0003] 【Background technique】 [0004] Synthetic Aperture Radar (SAR) is widely used in the field of earth science remote sensing, because it not only has all-day and all-weather characteristics, but also provides different information than infrared and visible light sensors. Therefore, the understanding and interpretation of SAR images has become a research hotspot. [0005] SAR image surface object classification is the application of pattern classification in SAR image processing. It completes the work of converting the image from the two-dimensional gray space to the target pattern space. The result of the classification is to divide the image into multiple different categories accordin...

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): G06K9/62G06N3/08G06T7/11
CPCG06N3/08G06T7/11G06T2207/20021G06F18/285G06F18/214
Inventor 焦李成屈嵘李晰张丹杨淑媛侯彪马文萍刘芳尚荣华张向荣唐旭马晶晶
Owner XIDIAN 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