CN108445489A - The method for determining tropical agriculture loss based on satellite remote sensing and unmanned aerial vehicle remote sensing - Google Patents
The method for determining tropical agriculture loss based on satellite remote sensing and unmanned aerial vehicle remote sensing Download PDFInfo
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- CN108445489A CN108445489A CN201810123670.0A CN201810123670A CN108445489A CN 108445489 A CN108445489 A CN 108445489A CN 201810123670 A CN201810123670 A CN 201810123670A CN 108445489 A CN108445489 A CN 108445489A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
- G01S13/9094—Theoretical aspects
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/95—Radar or analogous systems specially adapted for specific applications for meteorological use
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N2021/1793—Remote sensing
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
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Abstract
The present invention relates to agriculture setting loss technical fields, and in particular to a method of it efficiently, precisely comprehensively determining that tropical agriculture loses based on satellite remote sensing and unmanned aerial vehicle remote sensing, includes the following steps：It samples on the spot, determine Disasters Type and disaster loss grade, according to sample data, it chooses disaster-stricken front and back image information and is analyzed, extract disaster area distribution, from which further follow that disaster loss grade, disaster key area, fuzzy region are appropriately modified, extraction accuracy is improved, disaster distribution map is exported by ArcGIS, ENVI, carries out the determination of agricultural losses.
Description
Technical field
The present invention relates to agriculture setting loss technical fields, and in particular to it is a kind of efficiently, precisely comprehensively based on satellite remote sensing with
The method that unmanned aerial vehicle remote sensing determines tropical agriculture loss.
Background technology
In the loss determination process of tropical agriculture, after checking that setting loss personnel are connected to the task of checking, it is true to start casualty loss
Fixed work.This need of work largely surveys setting loss people, time, route, equipment.After reaching disaster-stricken scene, data acquisition is carried out
（Disaster-stricken type, disaster area, Disaster degree）, record a large amount of photo, video, written historical materials, it is final determine tropical agriculture by
The calamity extent of damage.When larger area is disaster-stricken, the setting loss work of devastated can not be completed within effective time by surveying setting loss personnel
Make；Meanwhile a large amount of dam site investigation setting loss cost is excessively high, can not accomplish region-wide setting loss.
Invention content
It is provided a kind of based on satellite remote sensing and nobody it is an object of the invention to overcome the deficiencies in the prior art
Machine remote sensing technology carries out precisely, efficiently, full the tropical agriculture disaster of large area distribution, polymorphic type disaster, different Disaster degrees
The method of the setting loss in face.
The technical proposal of the invention is realized in this way：
A method of based on satellite remote sensing and unmanned aerial vehicle remote sensing to determine that tropical agriculture loses, include the following steps：
Step 1）, sample collector to target area sampled on the spot, obtain the Disasters Type of sample, calamity selected by target area
Evil grade, location coordinate information, and sampling point feature is summarized after the completion of sampling；
The Disasters Type, disaster loss grade determine that the expert knowledge library is the industry of this field by expert knowledge library
Expert determines that the database of Disasters Type, grade, the location coordinate information are set by GPS positioning with its professional knowledge
It is standby to determine；
Step 2）, according to sample data, disaster-stricken front and back 1-2 days multispectral, radar images are chosen, in conjunction with weather information
Image is analyzed using image classification, spectrum analysis, terrain analysis technology, carries out disaster region detection, extraction disaster area
Domain is distributed；
Step 3）, for the testing result of step 2, the feature combination shadow that is described further combined with sample information and sample collector
As characteristic modification disaster is extracted as a result, and carrying out disaster loss grade delimitation；Disaster loss grade includes slight disaster, moderate disaster and severe
Disaster；
Step 4）, for disaster key area, fuzzy region, according to sampled result, by virtue of experience carry out parameter adjustment and people
Work is changed, and extraction accuracy is further increased；
Step 5）, obtain agricultural losses disaster distribution, disaster-stricken grade, and pass through ArcGIS, ENVI export disaster distribution map；
Step 6）, according to step 5）Obtained disaster distribution map determines agricultural losses.
In face of weather disaster can not obtain satellite remote-sensing image in time in the case of or when for emphasis disaster region, can be with
It sets out UAV flight's photographic equipment to take photo by plane to target area, obtains target area image, utilize photogrammetric technology system
Make orthography, threedimensional model, obtains the image information of target area；Using aforementioned achievement, in conjunction with sampling point information, selectivity
Artificial visual interpretation, Computer Vision Recognition, the disaster-stricken distribution in machine learning techniques extraction target area and disaster loss grade are carried out, and
Export disaster distribution map.
The positive effect of the present invention is：Technical staff can obtain target area by empirical value or a small amount of sampled point on the spot
The disaster-stricken image in domain, disaster-stricken type, disaster distribution, disaster loss grade determine the damaed cordition of target area；In this way, pole
It is big to avoid the setting loss mistake because of setting loss personnel subjective judgement, the initiation of field of investigation deficiency；It is improved by the technical method
Operating efficiency utilizes efficient, the comprehensive setting loss achievement of detailed data acquisition.
Specific implementation mode
A method of based on satellite remote sensing and unmanned aerial vehicle remote sensing to determine that tropical agriculture loses, include the following steps：
Step 1）, sample collector to target area sampled on the spot, obtain the Disasters Type of sample, calamity selected by target area
Evil grade, location coordinate information, and sampling point feature is summarized after the completion of sampling；
The Disasters Type, disaster loss grade determine that the expert knowledge library is the industry of this field by expert knowledge library
Expert determines that the database of Disasters Type, grade, the location coordinate information are set by GPS positioning with its professional knowledge
It is standby to determine；
Step 2）, according to sample data, disaster-stricken front and back 1-2 days multispectral, radar images are chosen, in conjunction with weather information
Image is analyzed using image classification, spectrum analysis, terrain analysis technology, carries out disaster region detection, extraction disaster area
Domain is distributed；
Step 3）, for the testing result of step 2, the feature combination shadow that is described further combined with sample information and sample collector
As characteristic modification disaster is extracted as a result, and carrying out disaster loss grade delimitation；Disaster loss grade includes slight disaster, moderate disaster and severe
Disaster；
Step 4）, for disaster key area, fuzzy region, according to sampled result, by virtue of experience carry out parameter adjustment and people
Work is changed, and extraction accuracy is further increased；
Step 5）, obtain agricultural losses disaster distribution, disaster-stricken grade, and pass through ArcGIS, ENVI export disaster distribution map；
Step 6）, according to step 5）Obtained disaster distribution map determines agricultural losses.
In face of weather disaster can not obtain satellite remote-sensing image in time in the case of or when for emphasis disaster region, can be with
It sets out UAV flight's photographic equipment to take photo by plane to target area, obtains target area image, utilize photogrammetric technology system
Make orthography, threedimensional model, obtains the image information of target area；Using aforementioned achievement, in conjunction with sampling point information, selectivity
Artificial visual interpretation, Computer Vision Recognition, the disaster-stricken distribution in machine learning techniques extraction target area and disaster loss grade are carried out, and
Export disaster distribution map.
Claims (2)
1. a kind of method based on satellite remote sensing and unmanned aerial vehicle remote sensing to determine tropical agriculture loss, which is characterized in that including with
Lower step：
Step 1）, sample collector to target area sampled on the spot, obtain the Disasters Type of sample, calamity selected by target area
Evil grade, location coordinate information, and sampling point feature is summarized after the completion of sampling；
The Disasters Type, disaster loss grade determine that the expert knowledge library is the industry of this field by expert knowledge library
Expert determines that the database of Disasters Type, grade, the location coordinate information are set by GPS positioning with its professional knowledge
It is standby to determine；
Step 2）, according to sample data, disaster-stricken front and back 1-2 days multispectral, radar images are chosen, in conjunction with weather information
Image is analyzed using image classification, spectrum analysis, terrain analysis technology, carries out disaster region detection, extraction disaster area
Domain is distributed；
Step 3）, for the testing result of step 2, the feature combination shadow that is described further combined with sample information and sample collector
As characteristic modification disaster is extracted as a result, and carrying out disaster loss grade delimitation；Disaster loss grade includes slight disaster, moderate disaster and severe
Disaster；
Step 4）, for disaster key area, fuzzy region, according to sampled result, by virtue of experience carry out parameter adjustment and people
Work is changed, and extraction accuracy is further increased；
Step 5）, obtain agricultural losses disaster distribution, disaster-stricken grade, and pass through ArcGIS, ENVI export disaster distribution map；
Step 6）, according to step 5）Obtained disaster distribution map determines agricultural losses.
2. the method according to claim 1 based on satellite remote sensing and unmanned aerial vehicle remote sensing to determine tropical agriculture loss,
It is characterized in that：In face of weather disaster can not obtain satellite remote-sensing image in time in the case of or for emphasis disaster region when, can
It is taken photo by plane to target area with setting out UAV flight's photographic equipment, obtains target area image, utilize photogrammetric technology
Orthography, threedimensional model are made, the image information of target area is obtained；Using aforementioned achievement, in conjunction with sampling point information, selection
Property carry out artificial visual interpretation, Computer Vision Recognition, the disaster-stricken distribution in machine learning techniques extraction target area and disaster loss grade,
And export disaster distribution map.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201810123670.0A CN108445489A (en) | 2018-02-07 | 2018-02-07 | The method for determining tropical agriculture loss based on satellite remote sensing and unmanned aerial vehicle remote sensing |
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| CN201810123670.0A Pending CN108445489A (en) | 2018-02-07 | 2018-02-07 | The method for determining tropical agriculture loss based on satellite remote sensing and unmanned aerial vehicle remote sensing |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109813286A (en) * | 2019-01-28 | 2019-05-28 | 中科光启空间信息技术有限公司 | A kind of lodging disaster remote sensing damage identification method based on unmanned plane |
| CN110533544A (en) * | 2019-08-28 | 2019-12-03 | 中国科学院遥感与数字地球研究所 | Crops freeze evil setting loss Claims Resolution method and system |
| CN110825105A (en) * | 2019-10-14 | 2020-02-21 | 武汉光庭信息技术股份有限公司 | Satellite film pattern spot inspection method and device based on unmanned aerial vehicle |
| CN111047566A (en) * | 2019-12-04 | 2020-04-21 | 昆明市滇池高原湖泊研究院 | Method for carrying out aquatic vegetation annual change statistics by unmanned aerial vehicle and multispectral satellite image |
| CN113222980A (en) * | 2021-06-01 | 2021-08-06 | 安徽建筑大学 | Flood disaster surveying method based on unmanned aerial vehicle platform |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109813286A (en) * | 2019-01-28 | 2019-05-28 | 中科光启空间信息技术有限公司 | A kind of lodging disaster remote sensing damage identification method based on unmanned plane |
| CN110533544A (en) * | 2019-08-28 | 2019-12-03 | 中国科学院遥感与数字地球研究所 | Crops freeze evil setting loss Claims Resolution method and system |
| CN110825105A (en) * | 2019-10-14 | 2020-02-21 | 武汉光庭信息技术股份有限公司 | Satellite film pattern spot inspection method and device based on unmanned aerial vehicle |
| CN110825105B (en) * | 2019-10-14 | 2023-03-10 | 武汉光庭信息技术股份有限公司 | Satellite film pattern spot inspection method and device based on unmanned aerial vehicle |
| CN111047566A (en) * | 2019-12-04 | 2020-04-21 | 昆明市滇池高原湖泊研究院 | Method for carrying out aquatic vegetation annual change statistics by unmanned aerial vehicle and multispectral satellite image |
| CN111047566B (en) * | 2019-12-04 | 2023-07-14 | 昆明市滇池高原湖泊研究院 | Method for carrying out aquatic vegetation annual change statistics by unmanned aerial vehicle and multispectral satellite image |
| CN113222980A (en) * | 2021-06-01 | 2021-08-06 | 安徽建筑大学 | Flood disaster surveying method based on unmanned aerial vehicle platform |
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Effective date of registration: 20191119 Address after: 450044 No. 301, Building No. 4, Dongruigu District, North Xincheng Road and South Chuangjie Street, Huiji District, Zhengzhou City, Henan Province Applicant after: ABDAS SPACE INFORMATION TECHNOLOGY Co.,Ltd. Address before: 570100 A1, A5002, Fuxing City, 32 Binhai Road, Longhua District, Haikou, Hainan. Applicant before: HAINAN YUNBAO REMOTE SENSING TECHNOLOGY Co.,Ltd. |
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Application publication date: 20180824 |