CN111191530A - Method, device and equipment for extracting town and bare land based on sentinel remote sensing data - Google Patents

Method, device and equipment for extracting town and bare land based on sentinel remote sensing data Download PDF

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CN111191530A
CN111191530A CN201911300261.4A CN201911300261A CN111191530A CN 111191530 A CN111191530 A CN 111191530A CN 201911300261 A CN201911300261 A CN 201911300261A CN 111191530 A CN111191530 A CN 111191530A
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remote sensing
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sentinel
water body
vegetation
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CN111191530B (en
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姜浩
郑琼
李丹
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Guangzhou Institute of Geography of GDAS
Southern Marine Science and Engineering Guangdong Laboratory Guangzhou
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Guangzhou Institute of Geography of GDAS
Southern Marine Science and Engineering Guangdong Laboratory Guangzhou
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Abstract

The embodiment of the application relates to a method, a device and equipment for extracting towns and bare places based on sentinel remote sensing data. The town and bare land extraction method based on the sentinel remote sensing data comprises the following steps: acquiring sentinel No. 2 optical remote sensing data of an area to be extracted, and reducing the short infrared band remote sensing data with 20 m resolution to 10m resolution; acquiring an improved normalized difference water body index MNDWI; acquiring a normalized vegetation index NDVI, and acquiring an annual synthesized normalized vegetation index according to the annual synthesized normalized vegetation index NDVI; extracting non-vegetation from the optical remote sensing data according to the annual synthesized normalized vegetation index; and removing the water body from the extracted non-vegetation according to the improved normalized difference water body index MNDWI to obtain the town and bare land extraction results. The town and bare area extraction method based on the sentinel remote sensing data can extract the town area and the bare area from the remote sensing image more accurately.

Description

Method, device and equipment for extracting town and bare land based on sentinel remote sensing data
Technical Field
The embodiment of the application relates to the technical field of remote sensing measurement, in particular to a method and a device for extracting towns and bare places based on sentinel remote sensing data.
Background
The bare land is the land with the surface layer being soil and basically not covered by plants; or the surface layer is rock and gravel, and the coverage area of the surface layer is more than or equal to 70 percent of the land. Because of no vegetation cover, the natural environment condition on bare land is bad, and the ecological environment of the area is seriously influenced. The urban bare land comprises unused, urban construction sites, bare land which is not treated in time after construction and the like. The urban bare soil is the main reason of ground dust emission, and PM2.5 source analysis shows that the dust emission is one of important sources of atmospheric particulate pollution; in addition, bare land is also not conducive to local soil and water conservation.
In recent years, with the development of economic society, the urban area of China is rapidly expanded, the surface coverage and the form of the earth are changed, and the climate, biochemistry and hydrology processes of local, regional and even global areas are directly influenced.
The extraction and research of the spatial distribution and the area of the towns and the bare lands have important significance for atmospheric environment protection, urban landscape beautification and land sustainable utilization, but no accurate means for extracting the towns and the bare lands exists at present.
Disclosure of Invention
The embodiment of the application provides a method and a device for extracting town and bare land based on sentinel remote sensing data and electronic equipment, which can more accurately extract town areas and bare land areas from remote sensing images.
In a first aspect, an embodiment of the application provides a method for extracting towns and bare places based on sentinel remote sensing data, which includes the steps:
acquiring sentinel No. 2 optical remote sensing data of an area to be extracted, and reducing the short infrared band remote sensing data with 20 m resolution to 10m resolution;
acquiring an improved normalized difference water body index MNDWI according to the green waveband data in the sentinel No. 2 optical remote sensing data and the reduced-scale short infrared waveband remote sensing data;
acquiring a normalized vegetation index NDVI according to near infrared band data and red light band remote sensing data in the sentinel No. 2 optical remote sensing data, and acquiring an annual synthesized normalized vegetation index according to the annual synthesized normalized vegetation index NDVI;
extracting non-vegetation from the optical remote sensing data according to the annual synthesized normalized vegetation index;
and extracting the water body from the optical remote sensing data according to the improved normalized difference water body index MNDWI, and removing the water body from the extracted non-vegetation by using the extracted water body as a mask to obtain town and bare land extraction results.
Optionally, extracting non-vegetation from the optical remote sensing data according to the annual synthesized normalized vegetation index includes:
denoising the normalized vegetation index NDVI by using median filtering;
using median filtering to train threshold T for normalized vegetation index NDVI, reference measured value and priori knowledgetrueAnd TfalseThe active contour method is adopted to segment the non-vegetation, and the formula is as follows:
Figure BDA0002320450880000021
wherein veg is vegetation, non-veg is non-vegetation, TtrueAnd TfalseTo set the threshold.
Optionally, extracting the water body from the optical remote sensing data according to the improved normalized difference water body index MNDWI includes:
and extracting the water body with the improved normalized difference water body index MNDWI larger than a set threshold value from the optical remote sensing data by adopting a threshold value method.
Optionally, after obtaining the extraction results of the town and the bare land, the method further comprises:
acquiring the pixel sizes of the extracted towns and bare places;
and removing the towns and the bare land with the pixels smaller than the set threshold value from the extracted pixels of the towns and the bare land.
Optionally, the downscaling of the short infrared band remote sensing data with the resolution of 20 meters into the resolution of 10 meters includes:
and resampling the short infrared band remote sensing data with the resolution of 20 meters by using a cubic sampling method to obtain the short infrared band remote sensing data with the resolution of 10 meters.
In a second aspect, an embodiment of the present application provides a town and bare area extraction device based on sentinel remote sensing data, including:
the first downscaling module is used for acquiring No. 2 optical remote sensing data of the sentinel, and downscaling the short infrared band remote sensing data with the resolution of 20 meters into the resolution of 10 meters;
the normalized difference water body index acquisition module is used for calculating an improved normalized difference water body index MNDWI according to the green waveband data in the sentinel number 2 optical remote sensing data and the reduced-scale short infrared waveband remote sensing data;
the normalized vegetation index acquisition module is used for acquiring a normalized vegetation index NDVI according to near infrared waveband data and red light waveband remote sensing data in the sentinel No. 2 optical remote sensing data and acquiring an annual synthesized normalized vegetation index according to the annual synthesized normalized vegetation index NDVI;
the first extraction module is used for extracting non-vegetation from the optical remote sensing data according to the annual synthesized normalized vegetation index;
and the second extraction module is used for extracting the water body from the optical remote sensing data according to the improved normalized difference water body index MNDWI, and removing the water body from the extracted non-vegetation by using the extracted water body as a mask to obtain town and bare land extraction results.
Optionally, the first extraction module includes:
a denoising unit, configured to denoise the normalized vegetation index NDVI by using median filtering;
a segmentation unit for applying median filtering to the normalized vegetation index NDVI, reference measured value, and priori knowledge training threshold TtrueAnd TfalseThe active contour method is adopted to segment the non-vegetation, and the formula is as follows:
Figure BDA0002320450880000031
wherein veg is vegetation, non-veg is non-vegetation, TtrueAnd TfalseTo set the threshold.
Optionally, the second extraction module includes:
and the extraction unit is used for extracting the water body with the improved normalized difference water body index MNDWI larger than a set threshold value from the optical remote sensing data by adopting a threshold value method.
Optionally, the method further includes:
a pixel size acquisition unit for acquiring pixel sizes of the extracted towns and bare places;
and the pixel removing unit is used for removing the towns and the bare lands of which the pixels are smaller than the set threshold value from the extracted pixels of the towns and the bare lands.
In a third aspect, an embodiment of the present application provides an electronic device, including a memory and a processor;
the memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a sentinel telemetry data-based town and bare land extraction method as described in the first aspect of an embodiment of the present application.
In the embodiment of the application, due to the fact that the towns and the bare lands have similar spectral characteristics, the vegetation in the remote sensing image is in highlight color, the water body is in dark color, the towns and the bare lands are between the towns and the bare lands, and the vegetation and the water body have obvious difference and can be identified together, the water body and the non-vegetation are removed from the sentinel No. 2 optical remote sensing data based on the improved normalized difference water body index MNDWI and the normalized vegetation index NDVI, and the towns and bare lands can be accurately extracted.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
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FIG. 1 is a flow chart of a town and bare land extraction method based on sentinel remote sensing data of an embodiment of the application shown in an exemplary embodiment;
FIG. 2 is a schematic diagram of a town and bare land extraction device based on sentinel remote sensing data according to an embodiment of the present application, shown in an exemplary embodiment;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application, shown in an exemplary embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
It should be understood that the embodiments described are only some embodiments of the present application, and not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without any creative effort belong to the protection scope of the embodiments in the present application.
The terminology used in the embodiments of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the embodiments of the present application. As used in the examples of this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the application, as detailed in the appended claims. In the description of the present application, it is to be understood that the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not necessarily used to describe a particular order or sequence, nor are they to be construed as indicating or implying relative importance. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as appropriate.
Further, in the description of the present application, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
As shown in fig. 1, fig. 1 is a flowchart of a town and bare land extraction method based on sentinel remote sensing data in an exemplary embodiment, which includes the following steps:
step S101: acquiring No. 2 optical remote sensing data of the sentinel, and reducing the short infrared band remote sensing data with the resolution of 20 meters into the resolution of 10 meters;
the Sentinel 2 optical remote sensing data come from a Sentinel 2 satellite (Sentinel-2), the European Bureau introduced the Golboni earth environment monitoring project in 2014, and the core part of the project is a Sentinel (Sentinel) series satellite. Each series of sentry satellites basically consists of two satellites so as to meet the revisit requirement and the coverage requirement of the satellites. The high-resolution optical satellite of the sentinel 2 satellite runs on an polar orbit and is mainly used for land monitoring services, such as vegetation, water and soil conservation, inland water channels, coastal area images and the like.
The main load multispectral imager of the sentinel-2 series satellite has 13 spectral bands from visible light to near infrared to short wave infrared, the spatial resolution is from 10m to 60m, and all unprecedented land ocean monitoring levels can be realized. In the remote sensing data of the sentinel No. 2 satellite, the resolution of the short-wave infrared band SWIR is 20 meters and is lower than that of other optical bands (10 meters), so that the traditional water body extraction method based on the data mostly depends on the water body index NDWI, and the identification effect is not high.
In the embodiment of the application, the short infrared band remote sensing data with the resolution of 20 meters in the sentinel # 2 optical remote sensing data is downscaled to the resolution of 10 meters, and the downscaling method may be resampling, for example, resampling by using a cubic sampling method, and in other examples, resampling may also be performed by using other methods.
Step S102: calculating an improved normalized difference water body index MNDWI according to the green waveband data in the sentinel No. 2 optical remote sensing data and the reduced-scale short infrared waveband remote sensing data;
the improved normalized difference water body index MNDWI (normalized NDWI, wherein NDWI (normalized difference Moisture index) is a normalized humidity index) can reveal the subtle characteristics of the water body more than NAWI, is easy to distinguish shadows from the water body and has more advantages in extracting urban water bodies.
In the improved MNDWI model, the spectral characteristics of the shadows of buildings and the like in a green light wave band and a near infrared wave band are similar to those of the water body, when the near infrared wave band is replaced by a middle infrared wave band, the contrast between the calculated water body and the building index can be obviously enhanced, the confusion degree of the water body and the building index is greatly reduced, and therefore the accurate extraction of the water body information in cities and towns is facilitated.
The improved normalized difference water body index MNDWI is calculated by the following formula:
Figure BDA0002320450880000051
where ρ isgreenRepresents a green light band; rhoswirRepresenting the short wave infrared band.
Step S103: acquiring a normalized vegetation index NDVI according to near infrared band data and red light band remote sensing data in the sentinel No. 2 optical remote sensing data, and acquiring an annual synthesized normalized vegetation index according to the annual synthesized normalized vegetation index NDVI;
the normalized Vegetation index NDVI (normalized Vegetation index) is one of important parameters reflecting growth and nutritional information of crops, and in one example, the normalized difference Vegetation index NDVI in the extraction results of the towns and the bare land is calculated according to the following formula;
Figure BDA0002320450880000052
where ρ isnirIs the near infrared reflectance value, predIs a red band reflectance value.
Annual synthetic normalized vegetation index NDVIannualFor the parameters representing the conditions of the annual vegetation index, there are various methods for calculating the annual combined normalized vegetation index NDVI, such as calculating the annual average, taking the annual minimum or annual maximum, etc., in the embodiment of the present application, the annual combined normalized vegetation index NDVIannualTaking the maximum value of the annual normalized vegetation index NDVI, wherein the specific calculation formula is as follows:
NDVIannual＝max(NDVI1,…,NDVIlast)
wherein NDVI1、NDVIlastFor each normalized vegetation index.
Step S104: extracting non-vegetation from the optical remote sensing data according to the annual synthesized normalized vegetation index;
step S105: and extracting the water body from the optical remote sensing data according to the improved normalized difference water body index MNDWI, and removing the water body from the extracted non-vegetation by using the extracted water body as a mask to obtain town and bare land extraction results.
In the embodiment of the application, due to the fact that the towns and the bare lands have similar spectral characteristics, the vegetation in the remote sensing image is in highlight color, the water body is in dark color, the towns and the bare lands are between the towns and the bare lands, and the vegetation and the water body have obvious difference and can be identified together, the water body and the non-vegetation are removed from the sentinel No. 2 optical remote sensing data based on the improved normalized difference water body index MNDWI and the normalized vegetation index NDVI, and the towns and bare lands can be accurately extracted.
In an exemplary embodiment, extracting non-vegetation from the optical remote sensing data according to the annual combined normalized vegetation index includes:
denoising the normalized vegetation index NDVI by using median filtering;
using median filtering to train threshold T for normalized vegetation index NDVI, reference measured value and priori knowledgetrueAnd TfalseThe active contour method is adopted to segment the non-vegetation, and the formula is as follows:
Figure BDA0002320450880000061
wherein veg is vegetation, non-veg is non-vegetation, TtrueAnd TfalseTo set the threshold.
The formula of median filtering denoising is as follows:
G(x,y)＝median(NDVIM)
wherein NDVIMIs the value of all normalized vegetation indices NDVI within 3 x 3 local regions.
In other examples, the method for denoising the normalized vegetation index NDVI may also be other filtering methods, such as gaussian filtering.
Pre-trained threshold TtrueAnd TfalseIn order to divide the thresholds of vegetation and non-vegetation according to the normalized vegetation index NDVI, an Active Contour method (Active Contour) is mainly used for solving the segmentation operation of a target object in an image, and the normalized vegetation index NDVI is divided through the Active Contour method and a pre-trained threshold, so that the non-vegetation can be extracted.
In an exemplary embodiment, extracting the water body from the optical remote sensing data according to the improved normalized difference water body index MNDWI includes:
extracting the water body with the improved normalized difference water body index MNDWI larger than a set threshold value from the optical remote sensing data by adopting a threshold value method according to the following formula:
MNDWI>TMNDWI
wherein, TMNDWITo set a threshold, or a trained threshold.
In other examples, the water body may also be segmented by the active contour method in the above embodiment according to the improved normalized difference water body index MNDWI, and the water body in the optical remote sensing data is extracted.
In an exemplary embodiment, after removing the water from the extracted non-vegetation to obtain the town and bare land extraction results, the method further comprises the step of removing noise, namely removing fragmented pixels. In a specific example, the removing of noise in the town and bare land extraction result specifically comprises the following steps:
acquiring the pixel sizes of the extracted towns and bare places;
and removing the towns and the bare land with the pixels smaller than the set threshold value from the extracted pixels of the towns and the bare land.
Specifically, the extraction result with pixels smaller than 20 may be removed, so as to obtain the final town and bare land extraction result.
Corresponding to the town and bare land extraction method based on the sentinel remote sensing data, the embodiment of the application also provides a town and bare land extraction device based on the sentinel remote sensing data, and the device can be installed on any intelligent terminal, and can be a computer, a server, an analysis device and the like. Because towns and bare lands have similar spectral characteristics, vegetation is highlight color in the remote sensing image, water is dark color, the towns and the bare lands are between the towns and the bare lands, and have obvious difference with the vegetation and the water, and can be identified together.
In an exemplary embodiment, as shown in fig. 2, the sentinel remote sensing data-based town and bare land extraction apparatus 200 includes:
the first downscaling module 201 is used for acquiring sentinel 2 optical remote sensing data and downscaling the short infrared band remote sensing data with 20 m resolution into 10m resolution;
the normalized difference water body index acquisition module 202 is used for calculating an improved normalized difference water body index MNDWI according to the green waveband data in the sentinel number 2 optical remote sensing data and the reduced-scale short infrared waveband remote sensing data;
the normalized vegetation index obtaining module 203 is configured to obtain a normalized vegetation index NDVI according to the near-infrared band data and the red-light band remote sensing data in the sentinel No. 2 optical remote sensing data, and obtain an annual synthesized normalized vegetation index according to the annual synthesized normalized vegetation index NDVI;
a first extraction module 204, configured to extract non-vegetation from the optical remote sensing data according to the annual synthesized normalized vegetation index;
and the second extraction module 205 is configured to extract a water body from the optical remote sensing data according to the improved normalized difference water body index MNDWI, and remove the water body from the extracted non-vegetation by using the extracted water body as a mask, so as to obtain town and bare land extraction results.
In one exemplary embodiment, the first extraction module includes:
a denoising unit, configured to denoise the normalized vegetation index NDVI by using median filtering;
a segmentation unit for applying median filtering to the normalized vegetation index NDVI, reference measured value, and priori knowledge training threshold TtrueAnd TfalseThe active contour method is adopted to segment the non-vegetation, and the formula is as follows:
Figure BDA0002320450880000081
wherein veg is vegetation, non-veg is non-vegetation, TtrueAnd TfalseTo set the threshold.
In an exemplary embodiment, the second extraction module includes:
and the extraction unit is used for extracting the water body with the improved normalized difference water body index MNDWI larger than a set threshold value from the optical remote sensing data by adopting a threshold value method.
In an exemplary embodiment, the method further comprises:
a pixel size acquisition unit for acquiring pixel sizes of the extracted towns and bare places;
and the pixel removing unit is used for removing the towns and the bare lands of which the pixels are smaller than the set threshold value from the extracted pixels of the towns and the bare lands.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Corresponding to the town and bare land extraction method based on the sentinel remote sensing data, the embodiment of the application also provides the electronic equipment applied to the town and bare land extraction device based on the sentinel remote sensing data, and the electronic equipment can be a computer, a mobile phone, a tablet computer and the like. Because towns and bare lands have similar spectral characteristics, vegetation is in highlight color in the remote sensing image, water is in dark color, the towns and the bare lands are between the towns and the bare lands, and have obvious difference with the vegetation and the water, the vegetation and the water can be identified together, the electronic equipment removes the water and non-vegetation from the sentinel No. 2 optical remote sensing data based on the improved normalized difference water index MNDWI and the normalized vegetation index NDVI, and can accurately obtain the towns and bare lands extraction results.
As shown in fig. 3, fig. 3 is a block diagram of an electronic device according to an exemplary embodiment of the present disclosure.
The electronic device includes: a processor 1200, a memory 1201, a display screen 1202 with touch functionality, an input device 1203, an output device 1204, and a communication device 1205. The number of the processors 1200 in the electronic device may be one or more, and one processor 1200 is taken as an example in fig. 3. The number of the memories 1201 in the electronic device may be one or more, and one memory 1201 is taken as an example in fig. 3. The processor 1200, the memory 1201, the display 1202, the input device 1203, the output device 1204, and the communication device 1205 of the electronic device may be connected by a bus or other means, and fig. 3 illustrates an example of a connection by a bus. In an embodiment, the electronic device may be a computer, a mobile phone, a tablet computer, an interactive smart tablet, a PDA (Personal Digital Assistant), an e-book reader, a multimedia player, and the like. In the embodiment of the present application, an electronic device is taken as an example of an interactive smart tablet to describe.
The memory 1201 is used as a computer-readable storage medium, and can be used to store a software program, a computer-executable program, and modules, such as a program of the sentinel-based town and bare land extraction method according to any embodiment of the present application, and program instructions/modules corresponding to the sentinel-based town and bare land extraction method according to any embodiment of the present application (for example, the first downscaling module 201, the normalized difference water body index acquisition module 202, the normalized vegetation index acquisition module 203, the first extraction module 204, the second extraction module 205, and the like). The memory 1201 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the device, and the like. Further, the memory 1201 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the memory 1201 may further include memory located remotely from the processor 1200, which may be connected to the devices through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The display screen 1202 may be a touch-enabled display screen, which may be a capacitive screen, an electromagnetic screen, or an infrared screen. Generally, the display screen 1202 is used for displaying data according to instructions of the processor 1200, and is also used for receiving touch operations applied to the display screen 1202 and sending corresponding signals to the processor 1200 or other devices. Optionally, when the display screen 1202 is an infrared screen, the display screen 1202 further includes an infrared touch frame, and the infrared touch frame is disposed around the display screen 1202, and may also be configured to receive an infrared signal and send the infrared signal to the processor 1200 or other devices. In other examples, the display screen 1202 may also be a display screen without touch functionality.
The communication means 1205 for establishing a communication connection with other devices may be a wired communication means and/or a wireless communication means.
The input device 1203 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function controls of the electronic apparatus, and may also be a camera for acquiring images and a sound pickup apparatus for acquiring audio data. The output device 1204 may include an audio device such as a speaker. It should be noted that the specific composition of the input device 1203 and the output device 1204 can be set according to actual situations.
The processor 1200 executes various functional applications and data processing of the device by running software programs, instructions and modules stored in the memory 1201, that is, implements the town and bare land extraction method based on sentinel remote sensing data described in any of the above embodiments.
The implementation process of the functions and actions of each component in the above device is specifically described in the implementation process of the corresponding step in the above method, and is not described herein again.
For the apparatus embodiment, since it basically corresponds to the method embodiment, reference may be made to the partial description of the method embodiment for relevant points. The above-described device embodiments are merely illustrative, wherein the components described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed solution. One of ordinary skill in the art can understand and implement it without inventive effort. The electronic equipment can be used for executing the town and bare land extraction method based on the sentinel remote sensing data provided by any embodiment, and has corresponding functions and beneficial effects. The implementation process of the functions and actions of each component in the device is specifically described in the implementation process of the corresponding step in the town and bare land extraction method based on the sentinel remote sensing data, and is not described herein again.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the embodiments of the application following, in general, the principles of the embodiments of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the embodiments of the application pertain. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the embodiments of the application being indicated by the following claims.
It is to be understood that the embodiments of the present application are not limited to the precise arrangements described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the embodiments of the present application is limited only by the following claims.
The above-mentioned embodiments only express a few embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for those skilled in the art, variations and modifications can be made without departing from the concept of the embodiments of the present application, and these embodiments are within the scope of the present application.

Claims (10)

1. A town and bare land extraction method based on sentinel remote sensing data is characterized by comprising the following steps:
acquiring sentinel No. 2 optical remote sensing data of an area to be extracted, and reducing the short infrared band remote sensing data with 20 m resolution to 10m resolution;
acquiring an improved normalized difference water body index MNDWI according to the green waveband data in the sentinel No. 2 optical remote sensing data and the reduced-scale short infrared waveband remote sensing data;
acquiring a normalized vegetation index NDVI according to near infrared band data and red light band remote sensing data in the sentinel No. 2 optical remote sensing data, and acquiring an annual synthesized normalized vegetation index according to the annual synthesized normalized vegetation index NDVI;
extracting non-vegetation from the optical remote sensing data according to the annual synthesized normalized vegetation index;
and extracting the water body from the optical remote sensing data according to the improved normalized difference water body index MNDWI, and removing the water body from the extracted non-vegetation by using the extracted water body as a mask to obtain town and bare land extraction results.
2. The sentinel remote sensing data-based town and open land extraction method according to claim 1, wherein extracting non-vegetation from the optical remote sensing data based on the annually synthesized normalized vegetation index comprises:
denoising the normalized vegetation index NDVI by using median filtering;
using median filtering to train threshold T for normalized vegetation index NDVI, reference measured value and priori knowledgetrueAnd TfalseThe active contour method is adopted to segment the non-vegetation, and the formula is as follows:
Figure FDA0002320450870000011
wherein veg is vegetation, non-veg is non-vegetation, TtrueAnd TfalseTo set the threshold.
3. The sentinel remote sensing data-based town and open land extraction method according to claim 1, wherein extracting water from the optical remote sensing data according to the improved normalized difference water body index MNDWI comprises:
and extracting the water body with the improved normalized difference water body index MNDWI larger than a set threshold value from the optical remote sensing data by adopting a threshold value method.
4. The town and bare land extraction method based on sentinel remote sensing data according to claim 1, wherein after obtaining the town and bare land extraction results, the method further comprises:
acquiring the pixel sizes of the extracted towns and bare places;
and removing the towns and the bare land with the pixels smaller than the set threshold value from the extracted pixels of the towns and the bare land.
5. The sentinel remote sensing data-based town and open land extraction method according to claim 1, wherein downscaling 20 m resolution short infrared band remote sensing data to 10m resolution comprises:
and resampling the short infrared band remote sensing data with the resolution of 20 meters by using a cubic sampling method to obtain the short infrared band remote sensing data with the resolution of 10 meters.
6. A town and bare land extraction device based on sentinel remote sensing data, the device comprising:
the first downscaling module is used for acquiring No. 2 optical remote sensing data of the sentinel, and downscaling the short infrared band remote sensing data with the resolution of 20 meters into the resolution of 10 meters;
the normalized difference water body index acquisition module is used for calculating an improved normalized difference water body index MNDWI according to the green waveband data in the sentinel number 2 optical remote sensing data and the reduced-scale short infrared waveband remote sensing data;
the normalized vegetation index acquisition module is used for acquiring a normalized vegetation index NDVI according to near infrared waveband data and red light waveband remote sensing data in the sentinel No. 2 optical remote sensing data and acquiring an annual synthesized normalized vegetation index according to the annual synthesized normalized vegetation index NDVI;
the first extraction module is used for extracting non-vegetation from the optical remote sensing data according to the annual synthesized normalized vegetation index;
and the second extraction module is used for extracting the water body from the optical remote sensing data according to the improved normalized difference water body index MNDWI, and removing the water body from the extracted non-vegetation by using the extracted water body as a mask to obtain town and bare land extraction results.
7. The sentinel remote sensing data-based town and open area extraction device according to claim 6, wherein the first extraction module comprises:
a denoising unit, configured to denoise the normalized vegetation index NDVI by using median filtering;
a segmentation unit for applying median filtering to the normalized vegetation index NDVI, reference measured value, and priori knowledge training threshold TtrueAnd TfalseThe active contour method is adopted to segment the non-vegetation, and the formula is as follows:
Figure FDA0002320450870000021
wherein veg is vegetation, non-veg is non-vegetation, TtrueAnd TfalseTo set the threshold.
8. The sentinel remote sensing data-based town and bare land extraction method according to claim 5, wherein the second extraction module comprises:
and the extraction unit is used for extracting the water body with the improved normalized difference water body index MNDWI larger than a set threshold value from the optical remote sensing data by adopting a threshold value method.
9. The sentinel remote sensing data-based town and open area extraction device according to claim 5, further comprising:
a pixel size acquisition unit for acquiring pixel sizes of the extracted towns and bare places;
and the pixel removing unit is used for removing the towns and the bare lands of which the pixels are smaller than the set threshold value from the extracted pixels of the towns and the bare lands.
10. An electronic device, comprising:
a memory and a processor;
the memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the sentinel telemetry data-based town and bare land extraction method of any of claims 1-5.
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