CN109141365A - Soil remediation method for monitoring state - Google Patents
Soil remediation method for monitoring state Download PDFInfo
- Publication number
- CN109141365A CN109141365A CN201810874499.7A CN201810874499A CN109141365A CN 109141365 A CN109141365 A CN 109141365A CN 201810874499 A CN201810874499 A CN 201810874499A CN 109141365 A CN109141365 A CN 109141365A
- Authority
- CN
- China
- Prior art keywords
- moment
- video information
- information
- soil
- high frequency
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Multimedia (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Image Processing (AREA)
Abstract
In order to avoid the characteristic of sensor oneself requirement fixed form installation monitors the blind area that may cause to blowdown, the present invention provides a kind of soil remediation method for monitoring state, for being monitored to forest farm or pasture by the reparation state of the soil of Pollution by Chemicals, comprising: (10) obtain the image information and the latitude and longitude information at each moment of the vegetation of soil region to be detected；(20) soil remediation state is determined.The present invention can be taken photo by plane by equipment such as unmanned planes and in the way of machine learning, obtain the growth characteristics such as the color of vegetation and state, and then carry out simple, quickly judgement to recovery situation of the soil after being polluted.
Description
Technical field
The present invention relates to environment monitoring techniques fields, more particularly, to a kind of soil remediation method for monitoring state.
Background technique
Soil pollution is the environmental problem being on the rise, and directly threatens the safety and soil ecology function of human food's health
The sustainable development of energy.It has attracted wide public concern about the toxicity of contaminated soil and with risk assessment, but has only combined
The interaction between soil pollutant and organism can be just effectively detected in the method for chemistry and biology.
Currently, Soil K+adsorption instrument point includes following several: 1) for the soil sample collector of soil pre-treatment, soil vibration sieve
Instrument, cutting ring, soil sieve, soil liquid sampler etc.；2) by soil nutrient detection soil EC based on, soil nutrient tacheometer,
Desk-top near-infrared soil nutrient tacheometer, hand-held soil nutrient tacheometer, nylon mesh, azotometer, ionometer, PH meter, atom
Absorption spectrophotometer, inscription hollow cathode lamp, second block steel cylinder, Atomic Absorption Spectrometer, atomic fluorescence spectrophotometer, mercury vapourmeter, drop
Determine instrument, gas chromatograph, spectrophotometer, conductivity meter, soil salt analyzer etc.；3) for the portable of soil moisture detection
Formula soil moisture content quick analyser, soil moisture content quick analyser, portable soil soil moisture content analyzer, soil moisture temperature tacheometer, drying
Method infrared moisture tester, soil moisture temperature tacheometer etc.；4) it is used for the digital soil hardometer of soil hardness detection, refers to
Pin type stratameter, soil density analyzer etc.；5) based on the P in soil H of soil acidity or alkalinity detection, pointer soil acid
Spend meter, digital soil acidometer etc..
In the prior art, application No. is the Chinese invention patent applications of CN201410220674.2 to disclose one kind based on object
Enterprise's rainwater discharge outlet monitoring system of networking, including field data acquisition device, Internet of Things monitoring center and emergency processing dress
It sets, the field data acquisition device includes monitor, water quality testing meter, integrated water pump；The Internet of Things monitoring center packet
Include central processing unit, controller, display, emergency alarm device, 3G warning module；The emergency treatment device includes recirculation water
Pump, several electrically operated valves, emergency lagoon, the field data acquisition device, display, controller and communication system are and centre
Device connection is managed, each electrically operated valve is connect with controller, and the emergency alarm device and 3G warning module pass through wired or nothing
Line mode is connected with communication system, and the monitor and water quality testing meter are mounted on the water inlet of integrated pump station.However, including
The prior art including this mode requires to be arranged many Soil K+adsorption equipment to scene, and the purchase cost of these equipment compared with
Height, installation cost are higher, and maintenance cost is higher.
In addition, the moment monitors the data that these equipment or regular monitoring these equipment obtain, it is difficult to which accurately reflecting it is
It is no the problem of soil secondary pollution occur.For current soil, pollution and repair process be typically all compared with
It is slow.Above-mentioned Soil K+adsorption equipment is unsuitable for being fixedly secured to these scenes, and otherwise efficiency is extremely low.And artificial scene inspection
The cost of survey is also higher, and detection activity is restricted by factors such as environment, geographical locations, thus what artificial on-site test obtained
Data accuracy leaves a question open.
Summary of the invention
In order to improve the efficiency of soil remediation detection and contamination monitoring, monitoring cost is reduced, the present invention provides a kind of soil
Earth repairs method for monitoring state, for being monitored to forest farm or pasture by the reparation state of the soil of Pollution by Chemicals, wraps
It includes:
(10) image information and the latitude and longitude information at each moment of the vegetation of soil region to be detected are obtained；
(20) soil remediation state is determined.
Further, the step (10) includes: and obtains the video information of the vegetation of soil region to be detected to be corrected,
And the corresponding latitude and longitude information of video information after being corrected, according to video information image-latitude and longitude information packet after correction.
Further, the step (20) includes: to carry out soil according to the video information image after correction-latitude and longitude information packet
The determination of earth reparation state.
Further, what described image-latitude and longitude information packet was obtains in the following way:
Assuming that the T0 moment, the T1 moment, the T2 moment ..., the Tn moment be corresponding n+1 consecutive hours in the video information
It carves, wherein n is the natural number greater than 4；
(101) framing is carried out to video information, the video information at T0 and T1 moment is converted into image information Img0 respectively
And Img1, and obtain the correction coefficient of low frequency sub-band signal and the correction coefficient of high frequency subband signals；
(102) according to the correction coefficient of the correction coefficient of low frequency sub-band signal and high frequency subband signals, when to T2
Carve ..., the video information at Tn moment is corrected；
(103) to the corrected T0 moment, the T1 moment, the T2 moment ..., the video information at Tn moment is ranked up；
(104) video information image-latitude and longitude information packet is generated.
Further, the step (101) includes:
(1011) wavelet transformation is carried out to Img0 and Img1 respectively, obtains being corresponding in turn in the low frequency at T0 moment and T1 moment
Subband signal L0, high frequency subband signals L1And high frequency subband signals H0, high frequency subband signals H1；
(1012) the correction coefficient C (x, y) of low frequency sub-band signal is calculatedL:
Wherein, the x and y respectively indicates the abscissa and ordinate of some pixel in the frame image at T0 moment, βmIt indicates
The mean value of correction matrix, ηmIndicate correction matrix variance, the correction matrix be withFor variance,It is equal
2 rank diagonal matrix B of value；
(1013) H ' is obtained by Gaussian filter to high frequency subband signals0And H '1:
(1014) for the frame image at T0 moment, the correction for being located at the high frequency subband signals of pixel of the position (x, y) is calculated
Coefficient C (x, y)H:
Wherein SδIndicate centered on (x, y),For the area in the circle domain of radius, the modulus value of D representing matrix A
Upper integer, A indicate following matrix:
Wherein i is the lower integer of the modulus value of matrix A.
Further, the step (102) includes:
(1021) to the T2 moment ..., the video information at Tn moment carry out wavelet transformation, respectively obtain and these videos believed
Cease one-to-one high frequency subband signals and low frequency sub-band signal；
(1022) to these high frequency subband signals for its correspondence at the time of video information in each point, with C (x,
y)HSubtract each other；
(1023) to these low frequency sub-band signals for its correspondence at the time of video information in each point, with C (x,
y)LSubtract each other；
(1024) at the time of by the above-mentioned high frequency subband signals by subtracting each other and low frequency sub-band signal according to its correspondence, respectively
Carry out wavelet inverse transformation, obtain with the corrected T2 moment ..., the video information at Tn moment.
Further, above-mentioned steps (103) include:
(1031) it records in above-mentioned correction course, corrected each high frequency subband signals；
(1032) each high frequency subband signals are subjected to convolution two-by-two according to chronological order；
(1033) median of convolution value is calculated；
(1034) the determining the smallest convolution value of absolute value of the difference with the median；
(1035) determination is corresponding with the smallest convolution value of the absolute value, comes subsequent video letter sequentially in time
Breath, as the T0 moment, the T1 moment, the T2 moment ..., the reference video information in this n+1 moment at Tn moment.
Further, step (104) includes:
(1041) by the reference video information, corresponding latitude and longitude information in video information is packaged with it；
(1042) information after the encapsulation is transmitted.
Further, the step (20) includes:
(201) it receives the information after encapsulating and unlocks, obtain reference video information and matched longitude and latitude letter
Breath；
(202) vegetation identification is carried out to reference video information in the way of machine learning；
(203) growth characteristics identification is carried out to the vegetation identified；
(204) it is compared according to the growth characteristics identified with reference to growth characteristics, when lower than threshold value or higher than threshold
When value, it is determined as soil restoration exception, the warp to match when exception with the reference video information of the information with appearance exception
Latitude information is content, is given a warning.
Further, the growth characteristics include: leaf color, plant trunk, trunk diameter.
The beneficial effect comprise that can be taken photo by plane by equipment such as unmanned planes and in the way of machine learning,
Growth characteristics and the states such as the color of vegetation are obtained, and then recovery situation of the soil after being polluted is carried out simple, quick
Judgement.Since this recovery process is very slow, it is being located at the domestic many places forest land progress in the Inner Mongol on a small scale through applicant
The monitoring frequency of test, unmanned plane can be primary for primary even two months one month, not only significantly reduces from energy consumption
Monitoring requirements, and monitoring cost is also greatly reduced from equipment purchase and maintenance cost.
Detailed description of the invention
Fig. 1 shows the flow chart of the method for the present invention.
Specific embodiment
As shown in Figure 1, preferred embodiment in accordance with the present invention, the present invention provides a kind of soil remediation condition monitoring sides
Method, for being monitored to forest farm or pasture by the reparation state of the soil of Pollution by Chemicals, comprising:
(10) image information and the latitude and longitude information at each moment of the vegetation of soil region to be detected are obtained；
(20) soil remediation state is determined.
Preferably, the step (10) includes: and obtains the video information of the vegetation of soil region to be detected to be corrected, and
The corresponding latitude and longitude information of video information after being corrected, according to video information image-latitude and longitude information packet after correction.
Preferably, the step (20) includes: to carry out soil according to the video information image after correction-latitude and longitude information packet
The determination of reparation state.
Preferably, what described image-latitude and longitude information packet was obtains in the following way:
Assuming that the T0 moment, the T1 moment, the T2 moment ..., the Tn moment be corresponding n+1 consecutive hours in the video information
It carves, wherein n is the natural number greater than 4；
(101) framing is carried out to video information, the video information at T0 and T1 moment is converted into image information Img0 respectively
And Img1, and obtain the correction coefficient of low frequency sub-band signal and the correction coefficient of high frequency subband signals；
(102) according to the correction coefficient of the correction coefficient of low frequency sub-band signal and high frequency subband signals, when to T2
Carve ..., the video information at Tn moment is corrected；
(103) to the corrected T0 moment, the T1 moment, the T2 moment ..., the video information at Tn moment is ranked up；
(104) video information image-latitude and longitude information packet is generated.
Preferably, the step (101) includes:
(1011) wavelet transformation is carried out to Img0 and Img1 respectively, obtains being corresponding in turn in the low frequency at T0 moment and T1 moment
Subband signal L0, high frequency subband signals L1And high frequency subband signals H0, high frequency subband signals H1；
(1012) the correction coefficient C (x, y) of low frequency sub-band signal is calculatedL:
Wherein, the x and y respectively indicates the abscissa and ordinate of some pixel in the frame image at T0 moment, βmIt indicates
The mean value of correction matrix, ηmIndicate correction matrix variance, the correction matrix be withFor variance,For mean value
2 rank diagonal matrix B；
(1013) H ' is obtained by Gaussian filter to high frequency subband signals0And H '1:
(1014) for the frame image at T0 moment, the correction for being located at the high frequency subband signals of pixel of the position (x, y) is calculated
Coefficient C (x, y)H:
Wherein SδIndicate centered on (x, y),For the area in the circle domain of radius, the modulus value of D representing matrix A
Upper integer, A indicate following matrix:
Wherein i is the lower integer of the modulus value of matrix A.
Preferably, the step (102) includes:
(1021) to the T2 moment ..., the video information at Tn moment carry out wavelet transformation, respectively obtain and these videos believed
Cease one-to-one high frequency subband signals and low frequency sub-band signal；
(1022) to these high frequency subband signals for its correspondence at the time of video information in each point, with C (x,
y)HSubtract each other；
(1023) to these low frequency sub-band signals for its correspondence at the time of video information in each point, with C (x,
y)LSubtract each other；
(1024) at the time of by the above-mentioned high frequency subband signals by subtracting each other and low frequency sub-band signal according to its correspondence, respectively
Carry out wavelet inverse transformation, obtain with the corrected T2 moment ..., the video information at Tn moment.
Preferably, above-mentioned steps (103) include:
(1031) it records in above-mentioned correction course, corrected each high frequency subband signals；
(1032) each high frequency subband signals are subjected to convolution two-by-two according to chronological order；
(1033) median of convolution value is calculated；
(1034) the determining the smallest convolution value of absolute value of the difference with the median；
(1035) determination is corresponding with the smallest convolution value of the absolute value, comes subsequent video letter sequentially in time
Breath, as the T0 moment, the T1 moment, the T2 moment ..., the reference video information in this n+1 moment at Tn moment.
Preferably, step (104) includes:
(1041) by the reference video information, corresponding latitude and longitude information in video information is packaged with it；
(1042) information after the encapsulation is transmitted.
Preferably, the step (20) includes:
(201) it receives the information after encapsulating and unlocks, obtain reference video information and matched longitude and latitude letter
Breath；
(202) vegetation identification is carried out to reference video information in the way of machine learning；
(203) growth characteristics identification is carried out to the vegetation identified；
(204) it is compared according to the growth characteristics identified with reference to growth characteristics, when lower than threshold value or higher than threshold
When value, it is determined as soil restoration exception, the warp to match when exception with the reference video information of the information with appearance exception
Latitude information is content, is given a warning.
Preferably, the growth characteristics include: leaf color, plant trunk, trunk diameter.
The above-described embodiments merely illustrate the principles and effects of the present invention, and is not intended to limit the present invention.It is any ripe
The personage for knowing this technology all without departing from the spirit and scope of the present invention, carries out modifications and changes to above-described embodiment.Cause
This, institute is complete without departing from the spirit and technical ideas disclosed in the present invention by those of ordinary skill in the art such as
At all equivalent modifications or change, should be covered by the claims of the present invention.
Claims (9)
1. a kind of soil remediation method for monitoring state, for the reparation state to forest farm or pasture by the soil of Pollution by Chemicals
It is monitored, comprising:
(10) image information and the latitude and longitude information at each moment of the vegetation of soil region to be detected are obtained；
(20) soil remediation state is determined.
2. the method according to claim 1, wherein the step (10) includes: to obtain soil region to be detected
The video information of vegetation be corrected, and the corresponding latitude and longitude information of video information after being corrected, after correction
Video information image-latitude and longitude information packet.
3. according to the method described in claim 2, it is characterized in that, the step (20) includes: according to the video letter after correction
Cease the determination that image-latitude and longitude information packet carries out soil remediation state.
4. according to the method described in claim 3, it is characterized in that, described image-latitude and longitude information packet be by such as lower section
What formula obtained:
Assuming that the T0 moment, the T1 moment, the T2 moment ..., the Tn moment be corresponding n+1 continuous moment in the video information,
Wherein n is the natural number greater than 4；
(101) to video information carry out framing, respectively by the video information at T0 and T1 moment be converted to image information Img0 and
Img1, and obtain the correction coefficient of low frequency sub-band signal and the correction coefficient of high frequency subband signals；
(102) according to the correction coefficient of the correction coefficient of low frequency sub-band signal and high frequency subband signals, to the T2 moment ..., Tn
The video information at moment is corrected；
(103) to the corrected T0 moment, the T1 moment, the T2 moment ..., the video information at Tn moment is ranked up；
(104) video information image-latitude and longitude information packet is generated.
5. according to the method described in claim 4, it is characterized in that, the step (101) includes:
(1011) wavelet transformation is carried out to Img0 and Img1 respectively, obtains being corresponding in turn in the low frequency sub-band at T0 moment and T1 moment
Signal L0, high frequency subband signals L1And high frequency subband signals H0, high frequency subband signals H1；
(1012) the correction coefficient C (x, y) of low frequency sub-band signal is calculatedL:
Wherein, the x and y respectively indicates the abscissa and ordinate of some pixel in the frame image at T0 moment, βmIndicate amendment square
The mean value of battle array, ηmIndicate correction matrix variance, the correction matrix be withFor variance,For 2 ranks of mean value
Diagonal matrix B；
(1013) H ' is obtained by Gaussian filter to high frequency subband signals0And H '1:
(1014) for the frame image at T0 moment, the correction coefficient C for being located at the high frequency subband signals of pixel of the position (x, y) is calculated
(x,y)H:
Wherein SδIndicate centered on (x, y),For the area in the circle domain of radius, the modulus value of D representing matrix A it is upper whole
Number, A indicate following matrix:
Wherein i is the lower integer of the modulus value of matrix A.
6. according to the method described in claim 5, it is characterized in that, the step (102) includes:
(1021) to the T2 moment ..., the video information at Tn moment carry out wavelet transformation, respectively obtain and these video informations one
One corresponding high frequency subband signals and low frequency sub-band signal；
(1022) each point in video information at the time of to these high frequency subband signals for its correspondence, with C (x, y)HPhase
Subtract；
(1023) each point in video information at the time of to these low frequency sub-band signals for its correspondence, with C (x, y)LPhase
Subtract；
(1024) it at the time of by the above-mentioned high frequency subband signals by subtracting each other and low frequency sub-band signal according to its correspondence, carries out respectively
Wavelet inverse transformation, obtain with the corrected T2 moment ..., the video information at Tn moment.
7. according to the method described in claim 6, it is characterized in that, above-mentioned steps (103) include:
(1031) it records in above-mentioned correction course, corrected each high frequency subband signals；
(1032) each high frequency subband signals are subjected to convolution two-by-two according to chronological order；
(1033) median of convolution value is calculated；
(1034) the determining the smallest convolution value of absolute value of the difference with the median；
(1035) determination is corresponding with the smallest convolution value of the absolute value, comes subsequent video information sequentially in time,
As the T0 moment, the T1 moment, the T2 moment ..., the reference video information in this n+1 moment at Tn moment.
8. the method according to the description of claim 7 is characterized in that step (104) includes:
(1041) by the reference video information, corresponding latitude and longitude information in video information is packaged with it；
(1042) information after the encapsulation is transmitted.
9. according to the method described in claim 8, it is characterized in that, the step (20) includes:
(201) it receives the information after encapsulating and unlocks, obtain reference video information and matched latitude and longitude information；
(202) vegetation identification is carried out to reference video information in the way of machine learning；
(203) growth characteristics identification is carried out to the vegetation identified；
(204) it is compared according to the growth characteristics identified with reference to growth characteristics, when lower than threshold value or higher than threshold value,
It is determined as soil restoration exception, the longitude and latitude letter to match when exception with the reference video information of the information with appearance exception
Breath is content, is given a warning.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810874499.7A CN109141365A (en) | 2018-08-02 | 2018-08-02 | Soil remediation method for monitoring state |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810874499.7A CN109141365A (en) | 2018-08-02 | 2018-08-02 | Soil remediation method for monitoring state |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109141365A true CN109141365A (en) | 2019-01-04 |
Family
ID=64798851
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810874499.7A Pending CN109141365A (en) | 2018-08-02 | 2018-08-02 | Soil remediation method for monitoring state |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109141365A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101382998A (en) * | 2008-08-18 | 2009-03-11 | 华为技术有限公司 | Testing device and method of switching of video scenes |
| WO2014050210A1 (en) * | 2012-09-26 | 2014-04-03 | 楽天株式会社 | Image processing device, image processing method, program, and information recording medium |
| CN104501720A (en) * | 2014-12-24 | 2015-04-08 | 河海大学常州校区 | Non-contact object size and distance image measuring instrument |
| CN106210559A (en) * | 2016-07-08 | 2016-12-07 | 北京邮电大学 | A kind of multisensor video fusion and the method and apparatus of noise reduction |
| CN106956778A (en) * | 2017-05-23 | 2017-07-18 | 广东容祺智能科技有限公司 | A kind of unmanned plane pesticide spraying method and system |
-
2018
- 2018-08-02 CN CN201810874499.7A patent/CN109141365A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101382998A (en) * | 2008-08-18 | 2009-03-11 | 华为技术有限公司 | Testing device and method of switching of video scenes |
| WO2014050210A1 (en) * | 2012-09-26 | 2014-04-03 | 楽天株式会社 | Image processing device, image processing method, program, and information recording medium |
| CN104501720A (en) * | 2014-12-24 | 2015-04-08 | 河海大学常州校区 | Non-contact object size and distance image measuring instrument |
| CN106210559A (en) * | 2016-07-08 | 2016-12-07 | 北京邮电大学 | A kind of multisensor video fusion and the method and apparatus of noise reduction |
| CN106956778A (en) * | 2017-05-23 | 2017-07-18 | 广东容祺智能科技有限公司 | A kind of unmanned plane pesticide spraying method and system |
Non-Patent Citations (1)
| Title |
|---|
| 肖武 等: "无人机遥感在矿区监测与土地复垦中的应用前景", 《中国矿业》 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109013677A (en) | A kind of soil organic pollutants-contaminated environment: A research review monitoring system | |
| CN110567510B (en) | Atmospheric pollution monitoring method, system, computer equipment and storage medium | |
| Arabi et al. | Integration of in-situ and multi-sensor satellite observations for long-term water quality monitoring in coastal areas | |
| Xu et al. | Recent greening (1981–2013) in the Mu Us dune field, north‐central China, and its potential causes | |
| Torbick et al. | Mapping inland lake water quality across the Lower Peninsula of Michigan using Landsat TM imagery | |
| IL267702A (en) | A system and a method for comprehensive monitoring, analysis and maintenance of water and equipment in swimming pools | |
| Reavie et al. | Diatom-based weighted-averaging transfer functions for Great Lakes coastal water quality: relationships to watershed characteristics | |
| CN106442420A (en) | Qualitative and quantitative combination water quality monitoring method | |
| Laing et al. | Factors influencing diatom distributions in circumpolar treeline lakes of northern Russia | |
| Wang et al. | Classifying diurnal changes of cyanobacterial blooms in Lake Taihu to identify hot patterns, seasons and hotspots based on hourly GOCI observations | |
| CN114974453B (en) | High-coverage reconstruction method for atmospheric carbon dioxide column concentration | |
| CN115524294A (en) | Water leaving type real-time intelligent remote sensing water quality monitoring method | |
| Devlin et al. | Monitoring and mapping of flood plumes in the Great Barrier Reef based on in situ and remote sensing observations | |
| Li et al. | Drivers to spatial and temporal dynamics of column integrated phytoplankton biomass in the shallow lake of Chaohu, China | |
| CN117111092A (en) | High-spatial-resolution remote sensing water quality detection method based on machine learning | |
| Xu et al. | Quantifying the impact of floods on bacillary dysentery in Dalian City, China, from 2004 to 2010 | |
| Scheibenreif et al. | Toward global estimation of ground-level no 2 pollution with deep learning and remote sensing | |
| Wang et al. | Mapping water clarity in North American lakes and reservoirs using Landsat images on the GEE platform with the RGRB model | |
| Ding et al. | NOx emissions in India derived from OMI satellite observations | |
| CN108982521A (en) | Visualize the horizontal detection device of soil health | |
| CN109141365A (en) | Soil remediation method for monitoring state | |
| CN109063873A (en) | A kind of soil restoration dynamic early-warning method | |
| CN109146862A (en) | Soil remediation condition intelligent detection device | |
| CN108917724A (en) | A kind of improvement detection system of contaminatedground | |
| Serino et al. | Sensing multi-agent system for anomaly detection on crop fields exploiting the phenological and historical context |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190104 |
|
| RJ01 | Rejection of invention patent application after publication |