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Moso bamboo forest distribution recognition method, device and equipment and storage medium

A recognition method and technology of moso bamboo, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problem of inability to accurately identify the distribution of moso bamboo forests, achieve the effect of obvious and easy-to-understand features and advantages, and improve recognition accuracy

Pending Publication Date: 2021-11-30
SANMING UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The invention provides a method, device, equipment and storage medium for identifying the distribution of moso bamboo forests, so as to improve the problem that the distribution of moso bamboo forests cannot be accurately identified in related technologies

Method used

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  • Moso bamboo forest distribution recognition method, device and equipment and storage medium
  • Moso bamboo forest distribution recognition method, device and equipment and storage medium
  • Moso bamboo forest distribution recognition method, device and equipment and storage medium

Examples

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Embodiment 1

[0080] see figure 1 , the first embodiment of the present invention provides a method for identifying the distribution of moso bamboo forests, which can be executed by an identification device for the distribution of moso bamboo forests. In particular, it is executed by one or more processors in the identification device to realize steps S1 to S7.

[0081] S1. Obtain remote sensing images of different phenological stages of bamboo leaves in the target area. Wherein, there are multiple sample areas in the target area. Multiple sample areas include Moso bamboo sample areas and other sample areas.

[0082] In this embodiment, the sample area is an area that is evenly distributed in the target area and has enough main types of ground objects in the area. The area of ​​each sample area is 20m*20m, and the center point of the sample area is positioned using GPS with Beidou positioning function. In other embodiments, sample areas of other shapes and sizes may also be used, and ot...

Embodiment 2

[0142] An embodiment of the present invention provides an identification device for the distribution of moso bamboo forests, which includes:

[0143] The image acquisition module 1 is used to acquire remote sensing images of different phenological stages of bamboo leaves in the target area. Wherein, there are multiple sample areas in the target area. Multiple sample areas include Moso bamboo sample areas and other sample areas.

[0144] The first calculation module 2 is used to calculate multiple vegetation indices and multiple texture features of remote sensing images of different phenological stages of bamboo leaves.

[0145] The first feature module 3 is used to extract multiple vegetation indices of multiple sample areas, and select the vegetation indices whose significance is less than the first preset value in the bamboo sample area and other sample areas as the first feature through the multiple comparison method.

[0146] The second feature module 4 is used to extrac...

Embodiment 3

[0183] An embodiment of the present invention provides an identification device for moso bamboo forest distribution, which includes a processor, a memory, and a computer program stored in the memory. The computer program can be executed by the processor to realize the method for identifying the distribution of moso bamboo forests as described in any one paragraph of the embodiment.

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Abstract

The embodiment of the invention provides a moso bamboo forest distribution recognition method and device, equipment and a storage medium, and relates to the technical field of information recognition and extraction of remote sensing images. The recognition method comprises the following steps: S1, collecting remote sensing images of different phyllostachys pubescens leaf phenological periods in a target area; s2, calculating a plurality of vegetation indexes and a plurality of texture features of remote sensing images of different phyllostachys pubescens leaves in phenological periods; s3 and S4, selecting a first feature and a second feature from the plurality of vegetation indexes and the plurality of texture features; and S5, based on the first feature, calculating a plurality of phenological features of the target area. And S6, selecting a third feature from the plurality of phenological features. And S7, fusing the first feature, the second feature and the third feature of the remote sensing image through a convolution method to obtain a to-be-identified image of the target area, and recognizing the moso bamboo forest distribution condition in the to-be-identified image through the classification model. The distribution condition of the phyllostachys edulis forest is identified through phenological characteristics, and the classification precision of the phyllostachys edulis forest is greatly improved.

Description

technical field [0001] The invention relates to the technical field of information identification and extraction of remote sensing images, in particular to a method, device, equipment and storage medium for identification of moso bamboo forest distribution. Background technique [0002] Moso bamboo forests play huge economic and ecological benefits by virtue of their fast growth, high yield and high economic value. Acquiring moso bamboo resources information quickly, accurately and efficiently, and monitoring the changes in bamboo resources in a timely manner will provide important macro guidance for quantitative monitoring of bamboo forest management effects, promotion of intensive bamboo forest management, and rational planning of regional bamboo industry development and construction. The development of bamboo industry is of great significance. [0003] Traditional bamboo resource monitoring technology has a long period and low resolution. For example, MODIS satellite im...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62
CPCG06F18/2411G06F18/214
Inventor 郭孝玉李增禄陈春乐康继谢伟娴刘健
Owner SANMING UNIV
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