Method for fusing unmanned aerial vehicle image and multispectral image based on Gram-Schmidt

A technology of multi-spectral imaging and unmanned aerial vehicle, applied in image data processing, graphic image conversion, instruments, etc., can solve problems such as inability to make full use of effective information and limitations

Active Publication Date: 2017-02-08
SUN YAT SEN UNIV
View PDF4 Cites 18 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the current image fusion technology is mostly limited to the fusion of single-band data and multi-band remote sensing images in terms of technology and data types; whi

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
  • Method for fusing unmanned aerial vehicle image and multispectral image based on Gram-Schmidt
  • Method for fusing unmanned aerial vehicle image and multispectral image based on Gram-Schmidt
  • Method for fusing unmanned aerial vehicle image and multispectral image based on Gram-Schmidt

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0048] figure 1 The above-mentioned fusion method of UAV image and multispectral image based on Gram-Schmidt transformation is given, including the following steps:

[0049] Through image preprocessing, including image registration, resampling to the same pixel size, and cropping in the same spatial range, we have prepared multispectral remote sensing images with low spatial resolution and three-band wireless remote sensing images with high spatial resolution in the same spatial range. man-machine optical image;

[0050] First, perform multiple linear regression on remote sensing images in the step "multiple linear regression" to obtain simulated UAV images;

[0051] Reconstruct the remote sensing image through the two steps of "intensity matching & averaging" and "reconstruction", and perform Gram-Schmidt transformation in the step "GS forward transformation" to obtain the GS component of the remote sensing image;

[0052] On the other hand, the UAV image is reconstructed t...

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 method for fusing unmanned aerial vehicle image and multispectral image based on Gram-Schmidt transformation, and method comprises the steps: two sets of individual multiband images including multispectral low spatial resolution remote-sensing images and tri-band high spatial resolution unmanned aerial vehicle visible light images with the same pixel sizes can be acquired through image preprocessing; multiple linear regression, reconstruction and Gram-Schmidt transformation are performed on the remote-sensing images, remote-sensing image GS constituent can be obtained, the same reconstruction and Gram-Schmidt transformation are performed on the unmanned aerial vehicle images, and unmanned aerial vehicle GS constituent can be obtained; gradient filtering is performed on the unmanned aerial vehicle GS constituent, texture information is obtained and is added to 1-4 GS constituent of the remote-sensing images with a certain weight; Gram-Schmidt inverse transformation is performed on an enhanced result, redundant information can be removed, and final fused images can be obtained. According to the invention, limitation of fusing single band panchromatic data and multispectral images in a fusing method in the prior art can be extended, diversity of fusing data can be added, and a fusing method having spectrum retention and information quality which are considered at the same time can be realized.

Description

technical field [0001] The present invention relates to the field of remote sensing image processing data fusion, more specifically, to a Gram-Schmidt-based fusion method of UAV images and multi-spectral images, which is a multi-band image and multi-band image fusion technology, and is a combination of Extended fusion techniques of multiple linear regression, gradient filtering and Gram-Schmidt transform. [0002] technical background [0003] Image fusion technology, especially pixel-level image fusion technology, can efficiently and accurately acquire multi-spectral image data with high spatial resolution, making up for the spatial resolution and spectral resolution of image products due to the limitation of sensor and remote sensing platform performance The lack of compatibility further meets the needs of more demanding remote sensing interpretation tasks, so this technology has also attracted much attention and has been greatly developed. At the technical level, from IHS...

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): G06T3/40
CPCG06T3/4061
Inventor 刘凯刘洋柳林李想
Owner SUN YAT SEN 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