Model training method and forest land change detection method and system and device and medium

A technology for change detection and model training, applied in character and pattern recognition, instrumentation, computing, etc., can solve problems such as low efficiency, low accuracy of interpretation results, inability to meet long-term and high-frequency change detection, etc. Low efficiency and low resolution

Active Publication Date: 2021-09-28
CHENGDU UNION BIG DATA TECH CO LTD
View PDF20 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, the main technology is to rely on manual visual interpretation of high-resolution remote sensing images, compare the two phases of remote sensing images, and manually outline the areas of forest changes. This method is inefficient, and the interpretation results are easily affected by subjective consciousness, resulting in The accuracy of the interpretation results is not high, and it cannot meet the needs of long-term and high-frequency change detection for a large range of forest resources

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Model training method and forest land change detection method and system and device and medium
  • Model training method and forest land change detection method and system and device and medium
  • Model training method and forest land change detection method and system and device and medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0074] Please refer to figure 1 , figure 1 For a schematic flowchart of a classification model training method, Embodiment 1 of the present invention provides a model training method, and the method includes:

[0075] Obtain remote sensing images of the target area;

[0076] Extracting a vegetation coverage area from the remote sensing image, dividing the vegetation coverage area into a forest land area and a non-forest land area, and classifying and labeling the forest land area and the non-forest land area to obtain a classification label map;

[0077] constructing a classification model, the input of the classification model is the remote sensing image of the preset area, and the output of the classification model is the forest land area and the non-forest area in the remote sensing image of the preset area;

[0078] Several characteristic data of the woodland area and the non-forest area are collected from the classification label map, and the classification model is tra...

Embodiment 2

[0115] The second embodiment of the present invention provides a forest land change detection method, and the method includes:

[0116] Use the classification model training method described in Embodiment 1 to train to obtain the third classification model and the fourth classification model;

[0117] Obtain the remote sensing image x of the area to be detected in the A period and the remote sensing image y of the to-be-detected area in the B period, wherein the A period is before the B period;

[0118] Inputting the remote sensing image x into the third classification model, and outputting the forestland area K in the remote sensing image x;

[0119] The remote sensing image y is input into the fourth classification model, and the forest area P in the remote sensing image y is output;

[0120] Based on the difference between the forest land area K and the forest land area P, a result map of forest land change detection of the to-be-detected area is obtained.

[0121] Wherei...

Embodiment 3

[0125] Please refer to figure 2 , figure 2 A schematic diagram of the composition of the model training system, the third embodiment of the present invention provides a model training system, the system includes:

[0126] a first obtaining unit, used for obtaining remote sensing images of the target area;

[0127] a classification label map obtaining unit, used for extracting a vegetation coverage area from the remote sensing image, dividing the vegetation coverage area into a forest land area and a non-forest land area, and classifying and labeling the forest land area and the non-forest land area, Get the classification label map;

[0128] a classification model construction unit, configured to construct a classification model, the input of the classification model is the input remote sensing image of the preset area, and the output of the classification model is the forest land area and the non-forest land area in the input remote sensing image;

[0129] A first traini...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a model training method, a forest land change detection method, a system, a device and a medium, and relates to the field of remote sensing image processing. The method includes obtaining a remote sensing image of a target area; extracting a vegetation coverage area from the remote sensing image, and converting the The vegetation coverage area is divided into a woodland area and a non-woodland area, and the woodland area and the non-woodland area are classified and marked to obtain a classification label map; a classification model is constructed, and the input of the classification model is a remote sensing image of a preset area, The output of the classification model is the woodland area and non-woodland area in the remote sensing image of the preset area; some characteristic data of the woodland area and the non-woodland area are collected from the classification label map, based on the The feature data trains the classification model, and the classification model trained by the present invention can quickly and accurately obtain the woodland area and the non-woodland area in the remote sensing image.

Description

technical field [0001] The invention relates to the field of remote sensing image processing, in particular, to a model training method and a forest land change detection method, system, device and medium. Background technique [0002] The forest resource change detection technology is a technology to obtain the forest land change area caused by external factors. At present, the main technology relies on artificial visual interpretation of high-resolution remote sensing images, comparing two periods of remote sensing images, and manually delineating areas of forest changes. This method is inefficient, and the interpretation results are easily affected by subjective consciousness, resulting in The accuracy of the interpretation results is not high, and it cannot meet the needs of long-term and high-frequency change detection for large-scale forest resources. SUMMARY OF THE INVENTION [0003] In order to solve the above problems, the present invention provides a model train...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62G06K9/34
CPCG06V20/188G06V10/267G06F18/214G06F18/241
Inventor 不公告发明人
Owner CHENGDU UNION BIG DATA TECH CO LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap