CN108053325A - A kind of agricultural insurance damage identification method based on crops remote sensing technology - Google Patents
A kind of agricultural insurance damage identification method based on crops remote sensing technology Download PDFInfo
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- CN108053325A CN108053325A CN201711238830.8A CN201711238830A CN108053325A CN 108053325 A CN108053325 A CN 108053325A CN 201711238830 A CN201711238830 A CN 201711238830A CN 108053325 A CN108053325 A CN 108053325A
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- 238000005070 sampling Methods 0.000 claims abstract description 6
- 238000011835 investigation Methods 0.000 claims description 6
- 238000012795 verification Methods 0.000 claims description 6
- 239000000284 extract Substances 0.000 claims description 5
- 238000010835 comparative analysis Methods 0.000 claims description 3
- 230000004069 differentiation Effects 0.000 claims description 3
- 230000012010 growth Effects 0.000 claims description 3
- 230000002452 interceptive effect Effects 0.000 claims description 3
- 230000008635 plant growth Effects 0.000 claims description 3
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/08—Insurance
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/28—Measuring arrangements characterised by the use of optical techniques for measuring areas
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
- G01C11/04—Interpretation of pictures
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract
The invention belongs to agricultural insurance technical fields, more particularly to carry out analysis of image data, a kind of agricultural insurance damage identification method based on crops remote sensing technology handled using space remote sensing technology, including field operation sampling, the corresponding Disaster degree of Bu Tong disaster-stricken grade that is obtained with definite remote sensing image interpretation of remote sensing image interpretation, the crops disaster of completing target area is distributed, the definite step of area and Disaster degree.There are a kind of agricultural insurance damage identification method based on crops remote sensing technology provided by the invention a needs different degrees of area is sampled, the advantages of a small amount of personnel is only needed to can be obtained by the whole circumstances of identical disaster-stricken grade to spot sampling, have saved substantial amounts of man power and material.
Description
Technical field
Analysis of image data, place are carried out the invention belongs to agricultural insurance technical field more particularly to using space remote sensing technology
A kind of agricultural insurance damage identification method based on crops remote sensing technology of reason.
Background technology
Setting loss link is surveyed in agricultural insurance, original method used to crops progress setting loss is mainly used with lower section
Method, first according to disaster-stricken warning message after, survey that personnel carry relevant instrument and evidence taking equipment rushes to the scene, in devastated
Disaster area is measured.And it takes pictures according to flow is surveyed.The disaster-stricken journey of target is obtained with reference to local agricultural experts
Degree, loss is determined according to Disaster degree and area.This mode survey there are it is apparent the shortcomings that, first, large area and big
, it is necessary to which substantial amounts of personnel go to complete to survey work when the disaster of scope occurs.Second, have no idea during survey to determine by
Whether disaster area domain is target of really accepting insurance, and there is the possibility more reported, and information accuracy is not guaranteed.
The content of the invention
The purpose of the present invention is exactly to survey existing for setting loss in the process substantial amounts of personnel to be needed to go for solution agricultural insurance
Into work is surveyed, the problem of information accuracy is low, and provide using new remote sensing technology combination GPS technology, geography information skill
Art carries out devastated quickly to analyze and extract interpreting, and accurately extracts disaster area and disaster-stricken distribution, and is adopted with scene
Based on the achievement of sample and remote Sensing Interpretation, a kind of agriculture based on crops remote sensing technology to disaster-stricken crops setting loss is finally completed
Industry insures damage identification method.
The technical solution adopted by the present invention is：
A kind of agricultural insurance damage identification method based on crops remote sensing technology, comprises the following steps：
Step 1）, field operation sampling：After receiving the disaster-stricken information of agricultural, being evenly distributed with randomness is selected according to disaster-stricken information
Sample areas sends the personnel that survey to carry and surveys equipment to sample site progress dam site investigation, gathers disaster-stricken scene information；Pass through
Observe sample site situation, collect disaster-stricken scene information formed sample area report, by disaster-stricken scene information and sample area report and
Shi Huichuan；
Step 2）, remote sensing image interpretation：Remote sensing technology personnel choose calamity by analyzing the condition of a disaster essential information, plant growth rule
More phase devastateds image prepares image as agricultural remote sensing setting loss after preceding calamity, extracts devastated crop growth cycle weather
Data by the disaster-stricken scene information of field data acquisition, are chosen most useful for the image for differentiating Different Crop, are folded with remote sensing image
Bonus point is analysed, and comparative analysis obtains target area image spectral information, is extracted target area crop specie, is obtained target area agriculture
Crop pattern map；Based on crops distribution map, the sample area provided according to dam site investigation personnel is reported, before selection calamity after calamity
Image is compared, using a sample information part as feature samples, another part as the verification sample for being used for Late Stage Verification,
Feature samples with image after calamity before calamity are combined, analyze before calamity spectrum after calamity, are suitble to automatically by computer and man-machine interactive
The disaster-stricken area of this crops and distribution and disaster-stricken grade are extracted in the means of differentiation, interpretation, and are passed through and verified that sample verifies
Remote Sensing Interpretation is as a result, determine disaster-stricken overall condition；
Step 3）, with reference to field reconnaissance situation, meteorological data, remote sensing image interpretation as a result, according to agricultural experts to scene photograph
Observation and interpretation, determine the corresponding Disaster degree of the disaster-stricken grade of difference that remote sensing image interpretation obtains, complete target area
Crops disaster distribution, area and Disaster degree determine.
Further, step 1）The disaster-stricken scene information of middle acquisition include scene photograph, coordinate information, agrotype, by
Calamity type, the information of disaster-stricken situation.
The beneficial effects of the invention are as follows：
By the agricultural insurance damage identification method provided by the invention based on crops remote sensing technology, pass through the image to disaster area
It is interpreted, the disaster area in disaster area and disaster-stricken geographical coordinate can be obtained, it, can after the target data accepted insurance in superposition
To obtain the actual loss situation of target.By image interpretation we can also obtain the degree in disaster area, it is only necessary to it is right
Different degrees of area is sampled, it is only necessary to which a small amount of personnel to spot sampling can be obtained by the whole of identical disaster-stricken grade
Situation has saved substantial amounts of man power and material.
Specific embodiment
The core of the present invention is to provide a kind of agricultural insurance damage identification method based on crops remote sensing technology.
A kind of agricultural insurance damage identification method based on crops remote sensing technology, comprises the following steps：
Step 1）, field operation sampling：After receiving the disaster-stricken information of agricultural, being evenly distributed with randomness is selected according to disaster-stricken information
Sample areas sends the personnel that survey to carry and surveys equipment to sample site progress dam site investigation, gathers disaster-stricken scene information；Pass through
Observe sample site situation, collect disaster-stricken scene information formed sample area report, by disaster-stricken scene information and sample area report and
Shi Huichuan；The disaster-stricken scene information includes scene photograph, coordinate information, agrotype, disaster-stricken type, the information of disaster-stricken situation
Step 2）, remote sensing image interpretation：Remote sensing technology personnel choose calamity by analyzing the condition of a disaster essential information, plant growth rule
More phase devastateds image prepares image as agricultural remote sensing setting loss after preceding calamity, extracts devastated crop growth cycle weather
Data by the disaster-stricken scene information of field data acquisition, are chosen most useful for the image for differentiating Different Crop, are folded with remote sensing image
Bonus point is analysed, and comparative analysis obtains target area image spectral information, is extracted target area crop specie, is obtained target area agriculture
Crop pattern map；Based on crops distribution map, the sample area provided according to dam site investigation personnel is reported, before selection calamity after calamity
Image is compared, using a sample information part as feature samples, another part as the verification sample for being used for Late Stage Verification,
Feature samples with image after calamity before calamity are combined, analyze before calamity spectrum after calamity, are suitble to automatically by computer and man-machine interactive
The disaster-stricken area of this crops and distribution and disaster-stricken grade are extracted in the means of differentiation, interpretation, and are passed through and verified that sample verifies
Remote Sensing Interpretation is as a result, determine disaster-stricken overall condition；
Step 3）, with reference to field reconnaissance situation, meteorological data, remote sensing image interpretation as a result, according to agricultural experts to scene photograph
Observation and interpretation, determine the corresponding Disaster degree of the disaster-stricken grade of difference that remote sensing image interpretation obtains, complete target area
Crops disaster distribution, area and Disaster degree determine.
In the above method, the mobile collection equipment based on remote sensing technology, the main scene for completing sample point positions, is disaster-stricken existing
Field acquisition, data storage, check analysis, data upload.On the other hand be for satellite-remote-sensing image service, it is main complete to by
The making and issue of the analysis result of the multispectral data in calamity area；The issue and analysis of the spatial datas such as target information.Control
Platform mainly completes the reception of mobile devices collect data, sorts and stores data into spatial database；Disaster area satellite
The display of image and the superposition of spatial data and analysis, the data report of disaster-stricken situation.
Claims (2)
1. a kind of agricultural insurance damage identification method based on crops remote sensing technology, which is characterized in that comprise the following steps：
Step 1）, field operation sampling：After receiving the disaster-stricken information of agricultural, being evenly distributed with randomness is selected according to disaster-stricken information
Sample areas sends the personnel that survey to carry and surveys equipment to sample site progress dam site investigation, gathers disaster-stricken scene information；Pass through
Observe sample site situation, collect disaster-stricken scene information formed sample area report, by disaster-stricken scene information and sample area report and
Shi Huichuan；
Step 2）, remote sensing image interpretation：Remote sensing technology personnel choose calamity by analyzing the condition of a disaster essential information, plant growth rule
More phase devastateds image prepares image as agricultural remote sensing setting loss after preceding calamity, extracts devastated crop growth cycle weather
Data by the disaster-stricken scene information of field data acquisition, are chosen most useful for the image for differentiating Different Crop, are folded with remote sensing image
Bonus point is analysed, and comparative analysis obtains target area image spectral information, is extracted target area crop specie, is obtained target area agriculture
Crop pattern map；Based on crops distribution map, the sample area provided according to dam site investigation personnel is reported, before selection calamity after calamity
Image is compared, using a sample information part as feature samples, another part as the verification sample for being used for Late Stage Verification,
Feature samples with image after calamity before calamity are combined, analyze before calamity spectrum after calamity, are suitble to automatically by computer and man-machine interactive
The disaster-stricken area of this crops and distribution and disaster-stricken grade are extracted in the means of differentiation, interpretation, and are passed through and verified that sample verifies
Remote Sensing Interpretation is as a result, determine disaster-stricken overall condition；
Step 3）, with reference to field reconnaissance situation, meteorological data, remote sensing image interpretation as a result, according to agricultural experts to scene photograph
Observation and interpretation, determine the corresponding Disaster degree of the disaster-stricken grade of difference that remote sensing image interpretation obtains, complete target area
Crops disaster distribution, area and Disaster degree determine.
2. the agricultural insurance damage identification method according to claim 1 based on crops remote sensing technology, it is characterised in that：It is described
Step 1）The disaster-stricken scene information of middle acquisition includes scene photograph, coordinate information, agrotype, disaster-stricken type, disaster-stricken situation
Information.
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| CN201711238830.8A CN108053325A (en) | 2017-11-30 | 2017-11-30 | A kind of agricultural insurance damage identification method based on crops remote sensing technology |
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| CN201711238830.8A CN108053325A (en) | 2017-11-30 | 2017-11-30 | A kind of agricultural insurance damage identification method based on crops remote sensing technology |
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Cited By (10)
| 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 |
| CN109886142A (en) * | 2019-01-28 | 2019-06-14 | 中科光启空间信息技术有限公司 | A kind of crops decomposition method based on SAR technology |
| CN110046542A (en) * | 2019-01-28 | 2019-07-23 | 中科光启空间信息技术有限公司 | A kind of method that field operation sampling improves remote Sensing Interpretation efficiency |
| CN110415130A (en) * | 2019-07-05 | 2019-11-05 | 中国平安财产保险股份有限公司 | Agricultural insurance Claims Resolution method, apparatus, equipment and computer readable storage medium |
| 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 |
| CN110688513A (en) * | 2019-08-15 | 2020-01-14 | 平安科技（深圳）有限公司 | Crop survey method and device based on video and computer equipment |
| CN110827266A (en) * | 2019-11-07 | 2020-02-21 | 航天信德智图（北京）科技有限公司 | Crop damage assessment method based on multi-stage image comparison |
| CN110827158A (en) * | 2019-11-07 | 2020-02-21 | 航天信德智图（北京）科技有限公司 | Loss evaluation method based on NDVI time series change |
| WO2021012898A1 (en) * | 2019-07-23 | 2021-01-28 | 平安科技（深圳）有限公司 | Artificial intelligence-based agricultural insurance surveying method and related device |
| CN113129258A (en) * | 2021-03-02 | 2021-07-16 | 成都正和德能风险管理咨询有限公司 | Historical image tracing method for insurance target |
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109886142B (en) * | 2019-01-28 | 2022-12-02 | 中科光启空间信息技术有限公司 | Crop interpretation method based on SAR technology |
| CN109886142A (en) * | 2019-01-28 | 2019-06-14 | 中科光启空间信息技术有限公司 | A kind of crops decomposition method based on SAR technology |
| CN110046542A (en) * | 2019-01-28 | 2019-07-23 | 中科光启空间信息技术有限公司 | A kind of method that field operation sampling improves remote Sensing Interpretation efficiency |
| 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 |
| CN110415130A (en) * | 2019-07-05 | 2019-11-05 | 中国平安财产保险股份有限公司 | Agricultural insurance Claims Resolution method, apparatus, equipment and computer readable storage medium |
| CN110415130B (en) * | 2019-07-05 | 2023-07-14 | 中国平安财产保险股份有限公司 | Agricultural insurance claim settlement method, apparatus, device and computer readable storage medium |
| WO2021012898A1 (en) * | 2019-07-23 | 2021-01-28 | 平安科技（深圳）有限公司 | Artificial intelligence-based agricultural insurance surveying method and related device |
| CN110688513A (en) * | 2019-08-15 | 2020-01-14 | 平安科技（深圳）有限公司 | Crop survey method and device based on video and computer equipment |
| CN110688513B (en) * | 2019-08-15 | 2023-08-18 | 平安科技（深圳）有限公司 | Crop investigation method and device based on video and computer equipment |
| 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 |
| CN110827266A (en) * | 2019-11-07 | 2020-02-21 | 航天信德智图（北京）科技有限公司 | Crop damage assessment method based on multi-stage image comparison |
| CN110827158A (en) * | 2019-11-07 | 2020-02-21 | 航天信德智图（北京）科技有限公司 | Loss evaluation method based on NDVI time series change |
| CN113129258A (en) * | 2021-03-02 | 2021-07-16 | 成都正和德能风险管理咨询有限公司 | Historical image tracing method for insurance target |
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