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Tree species identification method based on multi-source remote sensing of unmanned aerial vehicle

An identification method and UAV technology, applied in the field of remote sensing identification, can solve the problems of low identification accuracy, insufficient spatial information for tree species identification in remote sensing images, etc., and achieve the effect of increasing accuracy.

Active Publication Date: 2021-11-02
RES INST OF FOREST RESOURCE INFORMATION TECHN CHINESE ACADEMY OF FORESTRY
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AI Technical Summary

Problems solved by technology

[0007] The present invention provides a remote sensing image tree species identification space information is insufficient, the recognition accuracy is low, and solves the problem of image space feature recognition technology based on the comprehensive application of visible light remote sensing images and laser radar point clouds - a multi-source remote sensing tree species for drones recognition methods

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  • Tree species identification method based on multi-source remote sensing of unmanned aerial vehicle
  • Tree species identification method based on multi-source remote sensing of unmanned aerial vehicle
  • Tree species identification method based on multi-source remote sensing of unmanned aerial vehicle

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Embodiment Construction

[0037] Principles and features of the present invention are described below, and examples are only used to explain the present invention, and are not intended to limit the scope of the present invention.

[0038] Such as figure 1 As shown, the following steps are included in this implementation example:

[0039] A Obtain the visible light image and the lidar point cloud, preprocess the lidar point cloud and the visible light image respectively, and obtain the preprocessed lidar point cloud and visible light orthophoto;

[0040] The UAV is equipped with RIEGL VUX-1LR lidar sensor, which realizes high-speed acquisition of lidar point cloud data through the near-infrared (1550nm) laser beam and 330° field of view of the rotating mirror. It is equipped with Sony ILCE-6000 micro-single camera acquisition Visible light data, using PPK dynamic post-processing positioning technology to achieve a high positioning accuracy of 15mm, the data was collected during the vegetation growth se...

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Abstract

The invention discloses a tree species identification method based on multi-source remote sensing of an unmanned aerial vehicle, and the method comprises the steps: obtaining a visible light image and a laser radar point cloud, and carrying out the preprocessing of the laser radar point cloud and the visible light image; detecting the crown of the canopy height model of the laser radar point cloud through a local maximum value method, and segmenting the crown through a watershed method to obtain a segmented crown boundary; obtaining a crown data set and a sample data set by taking the segmented crown boundary as an outer boundary and taking a visible light orthoimage brightness value and a laser radar canopy height model (CHM) as features; and carrying out transfer learning and ensemble learning on the crown data set and the sample data set through a convolutional neural network, and then outputting a tree species identification result. The unmanned aerial vehicle visible light remote sensing image and the laser radar point cloud are comprehensively applied, the deep CNN model is adopted for transfer learning, deep convolutional neural network transfer learning and integrated learning are input for tree species identification, and the accuracy of unmanned aerial vehicle remote sensing tree species identification is improved.

Description

technical field [0001] The invention belongs to the technical field of remote sensing identification, and in particular relates to a method for identifying tree species using multi-source remote sensing of an unmanned aerial vehicle. Background technique [0002] Accurate identification of tree species is the premise of forest parameter extraction and calculation, which has far-reaching significance for the monitoring, evaluation, forest zoning and sustainable forest management of forest ecosystems and biodiversity. Traditional tree species identification mainly relies on ground survey methods to identify and identify tree species based on the characteristics of roots, stems, leaves, flowers, fruits, juices, and colors. Deep learning, especially convolutional neural network, has been popularized in many fields due to its excellent automatic extraction of high-level features and high recognition accuracy. It has also been applied in the tree species recognition of UAV-RGB ima...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/34G06K9/62G06N3/04G06N3/08G01S17/88
CPCG06N3/08G01S17/88G06N3/045G06F18/24
Inventor 陈巧陈永富徐志杨李华玉
Owner RES INST OF FOREST RESOURCE INFORMATION TECHN CHINESE ACADEMY OF FORESTRY
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