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

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...

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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 r

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