CN112417935A - Environment inspection system and method - Google Patents
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- CN112417935A CN112417935A CN201910851466.5A CN201910851466A CN112417935A CN 112417935 A CN112417935 A CN 112417935A CN 201910851466 A CN201910851466 A CN 201910851466A CN 112417935 A CN112417935 A CN 112417935A
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- 238000007689 inspection Methods 0.000 title claims abstract description 92
- 238000000034 method Methods 0.000 title claims description 24
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 86
- 238000001228 spectrum Methods 0.000 claims abstract description 30
- 230000007613 environmental effect Effects 0.000 claims abstract description 21
- 238000010606 normalization Methods 0.000 claims abstract description 6
- 238000000985 reflectance spectrum Methods 0.000 claims description 8
- 230000000875 corresponding effect Effects 0.000 description 36
- 238000009825 accumulation Methods 0.000 description 14
- 238000010586 diagram Methods 0.000 description 3
- 208000001490 Dengue Diseases 0.000 description 2
- 206010012310 Dengue fever Diseases 0.000 description 2
- 208000025729 dengue disease Diseases 0.000 description 2
- 241000256113 Culicidae Species 0.000 description 1
- 241000255925 Diptera Species 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
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- G—PHYSICS
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/13—Satellite images
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
- G06F16/5838—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content using colour
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
Abstract
An environmental inspection system, comprising: the system database stores green light reflection spectrum images, near infrared light reflection spectrum images and thermal images related to the inspection area; and a processor accessing the system database, the processor comprising: the normalization difference water body index image module is used for obtaining a normalization difference water body index image related to the inspection area according to the green light reflection spectrum image and the near infrared light reflection spectrum image; and the image judging module is used for judging a first target area according to the normalized difference water body index image, judging a second target area according to the thermal image and judging a third target area according to the first target area and the second target area.
Description
Technical Field
The invention relates to an environment inspection system and a method thereof, in particular to an environment inspection system and a method thereof for detecting a ponding area.
Background
In outdoor ponding areas (such as ponding containers, small water pits, etc.), larvae of dengue vector mosquitoes are liable to develop, and further, dengue epidemics develop. However, there is no accurate method for efficiently inspecting the water-accumulating area of the area to be inspected. Therefore, an environmental inspection system and method are needed to efficiently assist in detecting whether there is a water accumulation area in the area to be inspected.
Disclosure of Invention
In order to solve the above problems, an idea of the present invention is to provide an environmental inspection system and a method thereof, which can efficiently assist in detecting whether a waterlogged area exists in an area to be inspected.
Based on the above idea, the present invention provides an environment inspection system, comprising: the system database stores green light reflection spectrum images, near infrared light reflection spectrum images and thermal images related to the inspection area; and a processor accessing the system database, the processor comprising: a Normalized Difference Water Index (NDWI) image module for obtaining a Normalized Difference Water Index image associated with the inspection area according to the green light reflection spectrum image and the near infrared light reflection spectrum image; and the image judging module is used for judging a first target area in the inspection area according to the normalized difference water body index image, judging a second target area in the inspection area according to the thermal image and judging a third target area in the inspection area according to the first target area and the second target area.
In a preferred embodiment of the present invention, the green reflected spectrum image, the near infrared reflected spectrum image and the thermal image are taken from an aerial image.
In a preferred embodiment of the present invention, the system database stores color images associated with the inspection area; the processor comprises a display module, wherein the display module displays the color image on a display and correspondingly marks the first target area and/or the second target area and/or the third target area on the displayed color image.
In a preferred embodiment of the present invention, the system database stores color images associated with the inspection area; wherein the image determining module determines a shadow area on the color image, and the image determining module determines the second target area according to the shadow area; wherein the color image is associated with the inspection area.
In a preferred embodiment of the present invention, the image determining module determines the second target area according to the thermal image and the first target area.
In a preferred embodiment of the present invention, the image determining module determines the first target area according to at least one normalized difference water body index value corresponding to the first target area on the normalized difference water body index image; the image determining module determines the second target area according to at least one temperature value corresponding to the second target area on the thermal image.
In a preferred embodiment of the present invention, the image determining module determines the first target area according to a normalized difference water indicator threshold; wherein the image determining module determines the second target area according to the temperature threshold.
In a preferred embodiment of the present invention, the image determining module determines the third target area according to a first weighted value corresponding to the normalized difference water indicator image and a second weighted value corresponding to the thermal image.
In a preferred embodiment of the present invention, the third target region is a region where the first target region and the second target region are repeated.
According to another aspect of the present invention, there is provided an environment inspecting method, comprising: obtaining a normalized difference water body index image related to the inspection area according to the green light reflection spectrum image related to the inspection area and the near infrared light reflection spectrum image related to the inspection area; judging a first target area in the inspection area according to the normalized difference water body index image; judging a second target area in the inspection area according to the thermal image; and judging a third target area in the inspection area according to the first target area and the second target area.
In a preferred embodiment of the present invention, the green reflected spectrum image, the near infrared reflected spectrum image and the thermal image are taken from an aerial image.
In a preferred embodiment of the present invention, the environment inspection method further comprises correspondingly marking the first target area and/or the second target area and/or the third target area on a color image, wherein the color image is associated with the inspection area.
In a preferred embodiment of the present invention, the environment inspection method further comprises determining a shadow region on the color image; wherein the second target area is determined according to the shadow area; wherein the color image is associated with the inspection area.
In a preferred embodiment of the present invention, the second target area is determined according to the thermal image and the first target area.
In a preferred embodiment of the present invention, the third target area is determined according to the first target area, the second target area and a color image, wherein the color image is associated with the inspection area.
In a preferred embodiment of the present invention, the first target area is determined according to at least one normalized difference water body index value corresponding to the first target area on the normalized difference water body index image; wherein the second target area is determined according to at least one temperature value corresponding to the second target area on the thermal image.
In a preferred embodiment of the present invention, the first target area is determined according to a normalized difference water body index threshold; wherein the second target area is determined according to the temperature threshold.
In a preferred embodiment of the present invention, the third target area is determined according to a first weighting value corresponding to the normalized difference water indicator image and a second weighting value corresponding to the thermal image.
In a preferred embodiment of the present invention, the third target region is a region where the first target region and the second target region are repeated.
The foregoing aspects and other aspects of the invention are apparent from the following detailed description of non-limiting specific embodiments, which proceeds with reference to the accompanying figures.
Drawings
Fig. 1 is a schematic diagram of an embodiment of the environment inspection system of the present invention.
Fig. 2 is a flowchart of an embodiment of the environment inspection system of the present invention.
[ List of reference numerals ]
100 environment inspection system
110 system database
120 processor
122 normalized difference water body index image module
124 image judging module
126 display module
130 display
200 environment inspection method
220 step
250 step
Detailed Description
Referring to FIG. 1, a schematic diagram illustrating one embodiment of an environmental inspection system in accordance with the present invention is shown. In the embodiment shown in fig. 1, the environmental inspection system 100 includes a system database 110, a processor 120, and a display 130. The processor 120 accesses the system database 110, and the processor 120 further includes a normalized difference water indicator image module 122, an image determination module 124, and a display module 126. In the embodiment shown in fig. 1, the system database 110 stores a green light reflectance spectrum image, a near infrared light reflectance spectrum image, a thermal image, and a color image associated with an inspection area. In one embodiment, the green reflected spectrum image, the near infrared reflected spectrum image, the thermal image, and the color image are taken from an aerial image, such as an aerial image captured by an aerial sensor or aerial camera carried by an aerial vehicle such as an airplane, drone, satellite, hot-air balloon, and the like. Thus, the user can judge whether the ponding area exists in the area which is not easy to be detected (such as a roof, a construction site and other areas which are not easy to enter). In one embodiment, the aerial vehicle is positioned at an altitude sufficient to determine a water accumulation area above a certain size, such as a flying altitude of 100 m. A water accumulation area of more than a certain size is required because a water accumulation area of less than a certain size is omitted because it is easy to evaporate due to an insufficient amount of water and mosquito larvae are not easy to grow. In one embodiment, the camera or sensor used for aerial photography has a resolution of 3cm for color images, 20cm for thermal images, and 10cm for multispectral images. It should be understood that the flying height and the resolution of each image are only examples, and in different embodiments, different flying heights or resolutions of each image can be selected according to the requirements.
In various embodiments of the present invention, each of the modules included in the processor 120 may be a resource operated by hardware and software, the technical features of each module may be expressed by a plurality of program instructions, and the technical effect of each module may be realized by one or more processors executing the program instructions.
In the embodiment shown in fig. 1, the normalized difference water body indicator image module 122 can obtain the normalized difference water body indicator image associated with the inspection area according to the green light reflection spectrum image and the near infrared light reflection spectrum image. In one embodiment, the normalization difference water indicator image module 122 calculates each corresponding pixel on the green light reflection spectrum image and the near infrared light reflection spectrum image to obtain the normalization difference water indicator image associated with the inspection area. Each pixel of the correlated normalized difference water body index image corresponds to a normalized difference water body index numerical value. In one embodiment, the calculation formula of the normalized difference water body index value corresponding to each pixel of the normalized difference water body index image is as follows:
(Green-NIR)/(Green+NIR)
wherein, Green refers to Green light reflection spectrum image, and NIR refers to near infrared light reflection spectrum image.
In the embodiment shown in fig. 1, the image determining module 124 determines a first target area in the inspection area according to the normalized difference water indicator image, determines a second target area in the inspection area according to the thermal image, and determines a third target area in the inspection area according to the first target area and the second target area. In one embodiment, the third target region is a region where the first target region and the second target region overlap.
In one embodiment, the image determining module 124 determines the first target region according to at least one normalized difference water body indicator value corresponding to the first target region on the normalized difference water body indicator image. In one embodiment, the image determining module 124 determines the first target area according to the magnitude of the normalized difference water body indicator value corresponding to each pixel on the normalized difference water body indicator image. For example, pixels in the normalized difference water indicator image having a normalized difference water indicator value that is 25 percent higher (the percentage value is merely exemplary here, and in various embodiments, the percentage value may be adjusted as needed) may be labeled as the first target area (i.e., the possible water accumulation area). The water body has stronger reflection in a green light wave band and strong absorption in a near infrared light wave band, so that the possibility that the area with larger normalized difference water body index value in the normalized difference water body index image is the water accumulation area is higher.
In one embodiment, the image determining module 124 determines the second target area according to at least one temperature value corresponding to the second target area on the thermal image. In one embodiment, each pixel of the thermal image corresponds to a temperature value. The image determining module 124 determines the second target area according to the temperature value corresponding to each pixel on the thermal image. For example, pixels in the thermal image having a temperature value of 25 percent lower (the percentage value is merely exemplary, and in various embodiments, the percentage value may be adjusted as desired) may be labeled as the second target area (i.e., the possible water accumulation area). Since the temperature of the water accumulation region tends to be low, the region having a low temperature value in the thermal image is more likely to be the water accumulation region. In one embodiment, the temperature value is not the actual temperature of the area corresponding to the pixel. In one embodiment, the temperature value represents only the radiation temperature intensity of the region corresponding to the pixel.
In one embodiment, the image determining module 124 may determine the first target area according to the normalized difference water indicator image, and then determine the second target area on the thermal image according to the first target area. For example, the first target area may be mapped onto the thermal image, and the second target area may be determined according to the first target area on the thermal image. Therefore, other areas except the first target area on the thermal image do not need to be additionally judged whether to be the second target area, and therefore the whole judgment time can be saved.
In one embodiment, the image determination module 124 determines the first target region from the normalized difference water indicator image according to a normalized difference water indicator threshold. For example, a pixel in the normalized difference water body indicator image corresponding to a normalized difference water body indicator value higher than the normalized difference water body indicator threshold value is marked as a first target region (i.e., a possible water accumulation region). In one embodiment, the image determining module 124 determines the second target region from the thermal image according to a temperature threshold. For example, the corresponding pixels in the thermal image with temperature values below the temperature threshold may be labeled as the second target region (i.e., the possible water accumulation region). In different embodiments, the normalized difference water body index threshold and the temperature threshold may be predetermined values and may be adjusted automatically according to different conditions.
In one embodiment, the image determining module 124 determines the third target area according to the first weighting value corresponding to the normalized difference water indicator image and the second weighting value corresponding to the thermal image. The user can determine whether to increase the first weighted value corresponding to the normalized difference water body index image according to the judgment accuracy of the normalized difference water body index image, or determine whether to increase the second weighted value corresponding to the thermal image according to the judgment accuracy of the thermal image. Thus, if the determination accuracy of the normalized difference water body indicator image is higher, the image determination module 124 determines the third target area with a higher reference level for the second target area. In various embodiments, the user can determine the first weighting value and the second weighting value according to different factors such as season, terrain, building type, building area proportion, plant area proportion, and the like.
In the embodiment shown in fig. 1, the display module 126 can display a color image on the display 130, and can correspondingly mark the first target area and/or the second target area and/or the third target area on the displayed color image. Therefore, the user can more accurately judge the water accumulation area of the inspection area according to the first target area and/or the second target area and/or the third target area marked on the color image and the color image. In one embodiment, the display module 126 can display the color image on other displays, such as a user's mobile phone, a computer, a tablet computer, etc., but not limited thereto.
In one embodiment, the image determining module 124 determines a shadow area in the inspection area on the color image, and the image determining module 124 determines the second target area according to the shadow area. For example, the shadow area may be corresponding to the thermal image, and the temperature threshold of the corresponding shadow area on the thermal image may be adjusted to be a shadow area temperature threshold, and then the corresponding pixel in the non-shadow area on the thermal image, whose temperature value is lower than the temperature threshold, and the corresponding pixel in the shadow area on the thermal image, whose temperature value is lower than the shadow area temperature threshold, may be marked as the second target area.
Referring next to FIG. 2, a flow diagram illustrating one embodiment of an environment inspection method in accordance with the present invention is shown. In the embodiment shown in fig. 2, the environmental inspection method 200 begins with step 210 of obtaining a normalized difference water body indicator image associated with an inspection area according to a green light reflection spectrum image associated with the inspection area and a near infrared light reflection spectrum image associated with the inspection area. In one embodiment, the green light reflectance spectrum image and the near infrared light reflectance spectrum image are taken from aerial images.
Then, step 220 is performed to determine a first target area in the inspection area according to the normalized difference water body index image. In one embodiment, the area with the higher normalized difference water body index value is more likely to be the water accumulation area. In one embodiment, the first target region is determined according to at least one normalized difference water body indicator value corresponding to the first target region on the normalized difference water body indicator image. In one embodiment, the first target region is determined according to a normalized difference water body indicator threshold. For example, a corresponding pixel in the normalized difference water body index image, in which the normalized difference water body index value is higher than the normalized difference water body index threshold value, may be marked as the first target region.
Then, in step 230, a second target area within the inspection area is determined according to the thermal image. In one embodiment, the region of lower temperature value is more likely to be the stagnant zone. In one embodiment, the thermal image is taken from an aerial image. In one embodiment, the second target area is determined according to at least one temperature value corresponding to the second target area on the thermal image. In one embodiment, the second target area is determined according to a temperature threshold. For example, the corresponding pixels in the thermal image with temperature values below the temperature threshold may be labeled as the second target region. In one embodiment, the second target area is determined based on the thermal image and the first target area. In one embodiment, the first target area is mapped onto the thermal image, and the second target area is determined according to the first target area on the thermal image. Therefore, other areas except the first target area on the thermal image do not need to be additionally judged whether the area is the second target area, and therefore the whole judgment time can be saved.
Next, in step 240, a third target area within the inspection area is determined according to the first target area and the second target area. In one embodiment, a shadow area is determined on the color image associated with the inspection area, and then a second target area is determined according to the shadow area. For example, the shadow area may be corresponding to the thermal image, and the temperature threshold of the corresponding shadow area on the thermal image may be adjusted to be the shadow area temperature threshold, and then the corresponding pixel in the non-shadow area on the thermal image, whose temperature value is lower than the temperature threshold, and the corresponding pixel in the shadow area on the thermal image, whose temperature value is lower than the shadow area temperature threshold, may be marked as the second target area. In one embodiment, the third target region is a region where the first target region and the second target region overlap. In one embodiment, the third target area is determined according to a first weighting value corresponding to the normalized difference water indicator image and a second weighting value corresponding to the thermal image. In one embodiment, the third target area is determined according to the first target area, the second target area and a color image associated with the inspection area. For example, the user or the environment inspection system of the present invention can further determine a third target area that may be a waterlogging area according to the first target area and the second target area marked on the color image.
Then, in step 250, the first target area and/or the second target area and/or the third target area are correspondingly marked on the color image, wherein the color image is associated with the inspection area. Therefore, the user can more accurately judge the water accumulation area of the inspection area according to the first target area and/or the second target area and/or the third target area marked on the color image and the color image. In one embodiment, the color image is taken from an aerial image.
The environment inspection system and the method thereof according to the present invention have been described with reference to the above description and the accompanying drawings. It is to be understood that the embodiments of the invention are for purposes of illustration only and that various changes may be made without departing from the spirit and scope of the invention, which is to be construed in accordance with the substance defined by the claims. Therefore, it is intended that the present invention not be limited to the particular embodiments disclosed, but that the true scope and spirit of the invention be indicated by the following claims.
Claims (19)
1. An environment inspection system, comprising:
the system database stores green light reflection spectrum images, near infrared light reflection spectrum images and thermal images related to the inspection area; and
a processor accessing the system database, the processor comprising:
the normalization difference water body index image module is used for obtaining a normalization difference water body index image related to the inspection area according to the green light reflection spectrum image and the near infrared light reflection spectrum image; and
the image judgment module judges a first target area in the inspection area according to the normalized difference water body index image, judges a second target area in the inspection area according to the thermal image, and judges a third target area in the inspection area according to the first target area and the second target area.
2. The environmental inspection system according to claim 1, wherein the green light reflectance spectrum image, the near infrared light reflectance spectrum image, and the thermal image are taken from an aerial image.
3. The environmental inspection system according to claim 1, wherein the system database stores color images associated with the inspection area; the processor comprises a display module, wherein the display module displays the color image on a display and correspondingly displays the first target area and/or the second target area and/or the third target area on the displayed color image.
4. The environmental inspection system according to claim 1, wherein the system database stores color images associated with the inspection area; the image judging module judges a shadow area on the color image, and the image judging module judges the second target area according to the shadow area; wherein the color image is associated with the inspection area.
5. The environmental inspection system according to claim 1, wherein the image determination module determines the second target area based on the thermal image and the first target area.
6. The environmental inspection system according to claim 1, wherein the image determining module determines the first target area based on at least one normalized difference water body indicator value corresponding to the first target area on the normalized difference water body indicator image; the image judging module judges the second target area according to at least one temperature value corresponding to the second target area on the thermal image.
7. The environmental inspection system according to claim 1, wherein the image determination module determines the first target area based on a normalized differential water indicator threshold; the image judging module judges the second target area according to the temperature threshold value.
8. The environmental inspection system according to claim 1, wherein the image determination module determines the third target area based on a first weighting corresponding to the normalized differential water indicator image and a second weighting corresponding to the thermal image.
9. The environmental inspection system according to claim 1, wherein the third target area is an area where the first target area and the second target area overlap.
10. An environmental patrol method, comprising:
obtaining a normalized difference water body index image related to the inspection area according to the green light reflection spectrum image related to the inspection area and the near infrared light reflection spectrum image related to the inspection area;
judging a first target area in the inspection area according to the normalized difference water body index image;
judging a second target area in the inspection area according to the thermal image; and
and judging a third target area in the inspection area according to the first target area and the second target area.
11. The environmental inspection method according to claim 10, wherein the green light reflectance spectrum image, the near infrared light reflectance spectrum image, and the thermal image are taken from aerial images.
12. The environmental inspection method according to claim 10, further comprising correspondingly indicating the first target area and/or the second target area and/or the third target area on a color image, wherein the color image is associated with the inspection area.
13. The environment inspection method according to claim 10, further comprising determining a shadow region on the color image; wherein the second target area is determined according to the shadow area; wherein the color image is associated with the inspection area.
14. The environmental inspection method according to claim 10, wherein the second target area is determined according to the thermal image and the first target area.
15. The environmental inspection method according to claim 10, wherein the third target area is determined based on the first target area, the second target area and a color image associated with the inspection area.
16. The environment inspection method according to claim 10, wherein the first target area is determined according to at least one normalized difference water body indicator value corresponding to the first target area on the normalized difference water body indicator image; the second target area is determined according to at least one temperature value corresponding to the second target area on the thermal image.
17. The environment inspection method according to claim 10, wherein the first target area is determined according to a normalized difference water body index threshold; wherein the second target area is determined according to the temperature threshold.
18. The environmental inspection method according to claim 10, wherein the third target area is determined based on a first weighting corresponding to the normalized difference water indicator image and a second weighting corresponding to the thermal image.
19. The environmental inspection method according to claim 10, wherein the third target area is an area where the first target area and the second target area overlap.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113592863A (en) * | 2021-09-28 | 2021-11-02 | 南通市德信致远环境科技有限公司 | Urban road ponding detection method and system based on image processing |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103914692A (en) * | 2014-04-21 | 2014-07-09 | 山东科技大学 | Remote sensing recognition method for surface water systems around coal mine |
| US20170083747A1 (en) * | 2015-09-21 | 2017-03-23 | The Climate Corporation | Ponding water detection on satellite imagery |
| CN107330875A (en) * | 2017-05-31 | 2017-11-07 | 河海大学 | Based on the forward and reverse heterogeneous water body surrounding enviroment change detecting method of remote sensing images |
| CN108169142A (en) * | 2017-12-20 | 2018-06-15 | 环境保护部卫星环境应用中心 | Water colour exception method for rapidly positioning and device based on remote sensing image |
| CN109374537A (en) * | 2018-09-30 | 2019-02-22 | 中国科学院遥感与数字地球研究所 | The smelly identifying water boy method and device of urban black |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140119639A1 (en) * | 2012-10-31 | 2014-05-01 | Microsoft Corporation | Water-body classification |
| JP5950166B2 (en) * | 2013-03-25 | 2016-07-13 | ソニー株式会社 | Information processing system, information processing method of image processing system, imaging apparatus, imaging method, and program |
| JP6184901B2 (en) * | 2014-05-12 | 2017-08-23 | 株式会社日立ソリューションズ | Satellite image data processing apparatus, satellite image data processing system, satellite image data processing method and program |
| IL253260B (en) * | 2017-07-02 | 2021-07-29 | Manna Irrigation Ltd | Methods and systems for irrigation guidance |
| CN108564056B (en) * | 2018-04-25 | 2019-10-25 | 中国水利水电科学研究院 | A kind of method that remote sensing image identifying water boy extracts |
| CN109614924A (en) * | 2018-12-07 | 2019-04-12 | 江西洪都航空工业集团有限责任公司 | A kind of garbage on water detection method based on deep learning algorithm |
| CN110147746B (en) * | 2019-05-09 | 2020-11-17 | 武汉大学 | Method and system for rapidly extracting maximum and minimum possible surface water body ranges based on Sentinel-2 image |
-
2019
- 2019-08-23 TW TW108130283A patent/TWI726396B/en active
- 2019-09-10 CN CN201910851466.5A patent/CN112417935A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103914692A (en) * | 2014-04-21 | 2014-07-09 | 山东科技大学 | Remote sensing recognition method for surface water systems around coal mine |
| US20170083747A1 (en) * | 2015-09-21 | 2017-03-23 | The Climate Corporation | Ponding water detection on satellite imagery |
| CN107330875A (en) * | 2017-05-31 | 2017-11-07 | 河海大学 | Based on the forward and reverse heterogeneous water body surrounding enviroment change detecting method of remote sensing images |
| CN108169142A (en) * | 2017-12-20 | 2018-06-15 | 环境保护部卫星环境应用中心 | Water colour exception method for rapidly positioning and device based on remote sensing image |
| CN109374537A (en) * | 2018-09-30 | 2019-02-22 | 中国科学院遥感与数字地球研究所 | The smelly identifying water boy method and device of urban black |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113592863A (en) * | 2021-09-28 | 2021-11-02 | 南通市德信致远环境科技有限公司 | Urban road ponding detection method and system based on image processing |
| CN113592863B (en) * | 2021-09-28 | 2021-11-30 | 南通市德信致远环境科技有限公司 | Urban road ponding detection method and system based on image processing |
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|---|---|
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| TW202109362A (en) | 2021-03-01 |
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