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Image processing method for UAV aerial photography

An image processing and unmanned aerial vehicle technology, applied in the field of image processing, can solve the problems of affecting the quality of image compression, reducing the theoretical limit entropy, and obtaining an accurate reference for predictive coding. The effect of increasing the bandwidth compression ratio

Active Publication Date: 2020-12-01
ZHANGZHOU POWER SUPPLY COMPANY STATE GRID FUJIANELECTRIC POWER +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although this method can greatly improve the image compression ratio, when the image source to be compressed is a SAR image and the texture of the hyperspectral image is complex and changeable, the predictive coding cannot get an accurate reference, resulting in the theoretical limit entropy not being maximized The reduction affects the quality of image compression

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  • Image processing method for UAV aerial photography
  • Image processing method for UAV aerial photography
  • Image processing method for UAV aerial photography

Examples

Experimental program
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Effect test

Embodiment 1

[0052] See figure 1 , figure 1 A flow chart of an image processing method for aerial photography of a drone provided in an embodiment of the present invention, the method includes the following steps:

[0053] Step 1, using the UAV image acquisition device to collect the image in real time to generate an image data source;

[0054] Step 2, establishing an adaptation method for the UAV image data source compression interface;

[0055] Step 3, compressing the image data source to generate a digital transmission signal of the image data source;

[0056]Step 4: Send the digital transmission signal to the control terminal on the ground; and convert it into an image display.

[0057] Among them, the H.264 video compression coding standard can only compress the information in its own specific format, while the image and video information collected by the drone has a variety of formats, such as multi-frame video information, hyperspectral images, SAR images, etc., through The adap...

Embodiment 2

[0085] Specifically, see figure 2 , figure 2 It is a flowchart of an adaptive template prediction method provided by an embodiment of the present invention. This embodiment introduces in detail the adaptive template prediction method proposed by the present invention on the basis of the above embodiments, and the prediction method includes the following steps:

[0086] Step 1. Create and update an adaptive template

[0087] See image 3 , image 3 It is a schematic diagram of an adaptive template provided by an embodiment of the present invention. The establishment of the adaptive template includes the following steps:

[0088] Step 11, define the number of epitopes and the sequence number of epitopes of the adaptive template

[0089] Preferably, the number of epitopes in the adaptive template can be defined as 16; the number of epitopes in the adaptive template is 16, and the epitope numbers are arranged in order from 0 to 15. The smaller the sequence number, the high...

Embodiment 3

[0118] See Figure 4 , Figure 4 It is a flow chart of another prediction method for an adaptive template provided by an embodiment of the present invention. The forecasting method includes the following steps:

[0119] Step 1. Update the adaptive template corresponding to the current MB; see step 13 of the embodiment for details

[0120] Step 2. Adaptive prediction

[0121] After the adaptive template is updated, the pixel value of the current MB and all existing MB reconstruction values ​​in the adaptive template are used for adaptive texture prediction to solve the prediction residual.

[0122] Among them, see Figure 5 , Figure 5 A schematic diagram of adjacent reference pixels for adaptive texture prediction provided by an embodiment of the present invention. Select the reference pixels in the adaptive texture prediction, A, B, C, E are the surrounding pixels adjacent to the current pixel, that is, the reconstruction value corresponding to the current pixel in any ...

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Abstract

The invention relates to an image processing method for aerial photography of an unmanned aerial vehicle, which comprises the steps of: carrying out real-time acquisition on an image by utilizing unmanned aerial vehicle image acquisition equipment and generating an image data source; establishing an adaptation method of an unmanned aerial vehicle image data source compression interface; carrying out compression on the image data source and generating a digital transmission signal of the image; and transmitting the digital transmission signal to a control end and converting the digital transmission signal into the image for display. According to the invention, an adaptive template prediction method is adopted to carry out predictive encoding on an SAR (Synthetic Aperture Radar) image or a hyperspectral image so as to solve a problem that when an aerial photography image source of an existing unmanned aerial vehicle is a multi-source image and is complex and variable in texture, predictive encoding of an existing compression method cannot obtain accurate reference, resulting in that a theory limit entropy cannot be reduced to the greatest extent; and the image processing method for aerial photography of the unmanned aerial vehicle, which is provided by the embodiment, is good in prediction effect and can reduce the theory limit entropy and increase a bandwidth compression rate.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an image processing method for aerial photography of a drone. Background technique [0002] Digital cameras for real-time transmission in unmanned aerial systems replace film aerial reconnaissance cameras, high-definition digital televisions replace standard televisions, forward-looking infrared sensors, multi-spectral, hyperspectral images, and high-resolution, long-range, multi-mode Synthetic Aperture Radar (SAR) has been applied on UAV platforms one after another, so the image data from UAV reconnaissance tends to be multi-sourced, such as SAR images, hyperspectral images and videos. Due to the high flying height and fast speed of the UAV, and the large field of view of the airborne sensor, the reconnaissance targets (such as military vehicles, tanks, etc.) of interest in the UAV reconnaissance image have fewer pixels, and the intra-frame correlation is higher than th...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04N19/463H04N19/593
CPCH04N19/463H04N19/593
Inventor 陈杰沈滨胡钦俊张志林丁志君杨鹤猛罗瑜
Owner ZHANGZHOU POWER SUPPLY COMPANY STATE GRID FUJIANELECTRIC POWER