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Shadow detection method for farmland crop unmanned aerial vehicle image

A technology for farmland crops and shadow detection, which is applied in image enhancement, image analysis, image data processing, etc., and can solve problems such as difficult extraction and irregularities

Pending Publication Date: 2022-02-25
ZHONGBEI UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problem that the existing shadow detection algorithm is difficult to extract irregular and fragmented shadows in complex farmland scenes, the present invention provides a shadow detection method for UAV images of farmland crops

Method used

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  • Shadow detection method for farmland crop unmanned aerial vehicle image
  • Shadow detection method for farmland crop unmanned aerial vehicle image
  • Shadow detection method for farmland crop unmanned aerial vehicle image

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Experimental program
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Embodiment Construction

[0018] Shadow Detection Algorithm

[0019] The flow chart of the shadow detection algorithm is as follows: figure 1 As shown, there are three main steps. First, the three channels of the original RGB image are separated, and the gray value of each channel is combined and weighted to construct a new gray image; secondly, the maximum between-class variance method is used to calculate the threshold value of the new gray image and Segmentation, divide the image into shadow area and non-shade area, realize the preliminary detection of shadow area; finally use image morphology operation to optimize to output the final shadow detection result.

[0020] A New Grayscale Image Construction Method

[0021] Shadows are caused by objects partially or completely blocking the direct rays of the sun. Due to the image of the sun's altitude and azimuth, shadows in remote sensing images are inevitable. According to the analysis of the color characteristics of color remote sensing images, the ...

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Abstract

The invention relates to a shadow area detection method of a remote sensing image in a farmland environment, in particular to a shadow detection method of a farmland crop unmanned aerial vehicle image, and solves the problem that an existing shadow detection algorithm is difficult to extract irregular and fragmented shadows in a complex farmland scene. A novel grey-scale map is constructed by combining the shadow / non-shadow area color characteristics of an unmanned aerial vehicle image and utilizing a dual-channel difference value and G wave band enhancement, and automatic threshold segmentation is performed on the grey-scale map by using a maximum between-class variance method to obtain a shadow detection result. An experiment is carried out by using an unmanned aerial vehicle image obtained by aerial photography, and a result shows that a detection result of the method is closer to a real shadow, the average overall precision is 0.9868, and the average F1 score is 0.9567.

Description

technical field [0001] The invention relates to a method for detecting shadow areas of UAV remote sensing images in farmland scenes, in particular to a shadow detection algorithm for UAV images of crops. Background technique [0002] In recent years, with the wide application of remote sensing technology, remote sensing images have become an important means of surface target measurement and information extraction. UAV remote sensing technology provides strong support for the development of precision agricultural monitoring technology due to its advantages of high flexibility, high image data resolution, short operation cycle, and low cost, such as using UAV images for weed and crop identification, growth assessments, production forecasts, etc. As a ubiquitous phenomenon in agricultural UAV remote sensing images, shadows have a great negative impact on image quality and seriously interfere with later work such as plant identification, crop classification, and weed removal. ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/11G06T7/136G06T7/155G06T7/90G06V20/17G06V20/68G06V10/25G06V10/26
CPCG06T7/0002G06T7/11G06T7/136G06T7/155G06T7/90G06T2207/10004G06T2207/30188
Inventor 杨风暴王肖霞高敏刘晓霞马泽亮
Owner ZHONGBEI UNIV