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

Airport target detection method for high-resolution remote sensing image in complex background

A technology of remote sensing images and complex backgrounds, applied to computer parts, instruments, biological neural network models, etc., can solve problems such as insufficient detection accuracy, achieve accurate classification results, improve accuracy, and improve reliability

Active Publication Date: 2018-09-28
NORTHWESTERN POLYTECHNICAL UNIV
View PDF4 Cites 21 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In order to avoid the deficiencies of the prior art, the present invention proposes an airport target detection method for high-resolution remote sensing images under complex backgrounds, and overcomes the problem of insufficient detection accuracy of existing airport target detection methods

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
  • Airport target detection method for high-resolution remote sensing image in complex background
  • Airport target detection method for high-resolution remote sensing image in complex background
  • Airport target detection method for high-resolution remote sensing image in complex background

Examples

Experimental program
Comparison scheme
Effect test

specific Embodiment

[0041]1. Significance detection

[0042] The full convolutional network is used to detect the saliency of the remote sensing image, and the pixel-level saliency feature map of the remote sensing image is calculated. The steps are as follows:

[0043] (1) Preprocess the remote sensing image, transform the image in equal proportions to make its minimum size 600, and subtract the statistical mean value from each pixel value.

[0044] (2) Select the fully convolutional network FCN32s as the network basis, set the output category to 2, and remove the softmax layer, directly use the upsampled features as salient features, and set the loss function to the cross entropy of categories and probabilities.

[0045] (3) Use the FCN32s trained on the training set Pascal VOC 2012Segmentation database as the pre-training network, and use the saliency data set MSRA-1000 for network optimization

[0046] (4) Pass the remote sensing image into the trained fully convolutional saliency detection ...

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 present invention relates to an airport target detection method for a high-resolution remote sensing image in a complex background. The method comprises: using a full-convergence network to perform saliency detection on a remote sensing image, and using the improved LSD algorithm to extract line features of the remote sensing image; extracting saliency features and linear features to respectively divide the target candidate region, and taking the region simultaneously satisfying the saliency features and the linear features as a target candidate region; and extracting depth features of thecorresponding image in a candidate region by using the convolution network, converting the two-dimensional matrix features with different sizes into one-dimensional features with the same length by using the ROI Pooling network, calculating the target probability and the positional offset of the candidate region through two independent fully connected networks, and detecting an airport target inthe remote sensing image. According to the method provided by the present invention, priori knowledge such as the airport saliency, the parallel straight runway and the like is used to generate a small number of candidate regions, the difficulty of the test can be significantly reduced, the extracted candidate regions are more accurate, and the accuracy of detection and the positional accuracy ofmarking the airport region are improved.

Description

technical field [0001] The invention belongs to a method for detecting airport targets in remote sensing images, and relates to a method for detecting airport targets in high-resolution remote sensing images under complex backgrounds, in particular to a method for detecting airport targets in high-resolution remote sensing images with complex backgrounds. Background technique [0002] Traditional airport detection algorithms use airport-specific prior knowledge to extract airport areas, which mainly include airport detection algorithms based on line features and color texture features and airport detection algorithms based on salient features. The airport detection algorithm based on line features and color texture features uses the prior knowledge of airport runway shape and texture to extract corresponding features from the image, and then segments the airport area, and finally uses a classifier to filter out false targets in the segmented area. This type of method is simp...

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): G06K9/00G06K9/62G06N3/04
CPCG06V20/176G06V2201/07G06N3/045G06F18/24
Inventor 李映刘凌毅崔凡呼延烺
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