CN115131616A - Classification method, device, equipment and storage medium of land use type - Google Patents
Classification method, device, equipment and storage medium of land use type Download PDFInfo
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Abstract
The application provides a method and a device for classifying land use types, electronic equipment and a storage medium. The method comprises the following steps: obtaining remote sensing data and land use type classification results of at least two time phases of a target area in a historical time period; according to the remote sensing data and the land use type classification results of at least two time phases in the historical time period, counting to obtain a remote sensing index threshold value of each land use type corresponding to each time phase; acquiring remote sensing data of at least two time phases of a target area in a current time period; wherein the at least two phases in the historical period comprise at least two phases in the current period; and obtaining a land use type classification result of the target area in the current time period according to the remote sensing data of at least two time phases in the current time period and the remote sensing index threshold corresponding to each time phase. The scheme can improve the classification precision of the land use types, has higher reliability and has important significance for monitoring the land use condition.
Description
Technical Field
The application relates to the technical field of remote sensing, in particular to a method and a device for classifying land utilization types, electronic equipment and a storage medium.
Background
Land is an important component of earth resources. With the rapid growth of population, the available resources of land are less and less, so that the land utilization condition is more concerned in various countries around the world. The characteristics of timeliness, comprehensiveness and the like of the remote sensing technology enable rapid and accurate monitoring of the land utilization condition to be realized.
However, in actual monitoring and classification, due to the complexity of the land feature types and the instability of remote sensing image imaging and other factors, it is difficult to completely automatically and intelligently classify the land use types, and the accuracy of automatic interpretation is often lower than the result of visual interpretation. If the visual interpretation is performed in order to improve the accuracy, a large amount of energy and time are consumed for the visual interpretation in a large area, and the standards of different identification persons are not completely unified, which may bring a certain deviation to the identification result. Therefore, how to rapidly and accurately classify the land use types is a technical problem which needs to be solved in the field.
Disclosure of Invention
The application provides a method and a device for classifying land use types, electronic equipment and a storage medium, which can accurately classify the land use types.
In a first aspect, the present application provides a method for classifying land use types, comprising:
obtaining remote sensing data and land use type classification results of at least two time phases of a target area in a historical time period;
according to the remote sensing data and the land use type classification results of at least two time phases in the historical time period, counting to obtain a remote sensing index threshold value of each land use type corresponding to each time phase;
acquiring remote sensing data of at least two time phases of a target area in a current time period; wherein the at least two phases in the historical period comprise at least two phases in the current period;
and obtaining a land use type classification result of the target area in the current time period according to the remote sensing data of at least two time phases in the current time period and the remote sensing index threshold corresponding to each time phase.
In a possible implementation manner, the counting, according to the remote sensing data and the land use type classification result of at least two time phases in the historical time period, to obtain the remote sensing index threshold of each land use type corresponding to each time phase, includes:
s1, preprocessing the data;
aiming at the remote sensing data of each time phase, carrying out cloud detection on each remote sensing image of the current time phase, and removing an invalid value of the cloud; splicing the remote sensing images which can not completely cover the target area to obtain the remote sensing images which can completely cover the target area; averaging a plurality of remote sensing images capable of completely covering a target area to serve as a remote sensing image of the current time phase;
cutting the remote sensing image of the current time phase by using the vector data of the target area to obtain the remote sensing image of the current time phase of the target area;
s2, determining remote sensing index thresholds corresponding to the remote sensing indexes of the land use types;
calculating the remote sensing index of each pixel in the remote sensing image of the current time phase of the target area;
obtaining the land use type of each pixel according to the land use type classification result of the current time phase of the target area;
counting to obtain a remote sensing index threshold value of each land utilization type corresponding to the current time according to the remote sensing index and the land utilization type of each pixel;
and processing the remote sensing data and the land use type classification results of at least two time phases in the historical time period based on the steps S1 and S2 to obtain the remote sensing index threshold value of each land use type corresponding to each time phase.
In one possible implementation, the remote sensing index includes: one or more of a normalized vegetation index NDVI, a normalized construction index NDBI, and a normalized water body index NDWI;
NDVI＝(NIR-R)/(NIR+R)；
NDBI＝(MIR-NIR)/(MIR+NIR)；
NDWI＝(G-NIR)/(G+NIR)；
wherein G, R, NIR and MIR respectively represent green, red, near red and mid-red wave bands.
In a possible implementation manner, the obtaining, according to the remote sensing data of at least two time phases in the current time period and the remote sensing index threshold corresponding to each time phase, a land use type classification result of the target area in the current time period includes:
preprocessing the remote sensing data of at least two time phases in the current time period to obtain remote sensing images of all the time phases in the current time period of a target area;
aiming at the remote sensing image of each time phase in the current time period, adopting a remote sensing index threshold value corresponding to the time phase to identify the land utilization type of each pixel;
if the land utilization types of the same pixel at different time phases are the same, classifying the pixel into the land utilization type; and by analogy, the land use type classification result of the target area in the current time period is obtained.
In one possible implementation, the land use types include one or more of a body of water, a field, a shrub, a landscape site, and an artificial surface.
In one possible implementation, the remote sensing data is L1C data of a Sentinel-2B satellite.
In a second aspect, the present application provides a soil utilization type sorting device, comprising:
the acquisition module is used for acquiring remote sensing data and land utilization type classification results of at least two time phases of a target area in a historical time period;
the statistical module is used for obtaining remote sensing index threshold values of all land utilization types corresponding to each time phase through statistics according to the remote sensing data and the land utilization type classification results of at least two time phases in the historical time period;
the acquisition module is further used for acquiring remote sensing data of at least two time phases of the target area in the current time period; wherein the at least two phases in the historical period comprise at least two phases in the current period;
and the classification module is used for obtaining a land utilization type classification result of the target area in the current time period according to the remote sensing data of at least two time phases in the current time period and the remote sensing index threshold corresponding to each time phase.
In a possible implementation manner, the statistical module is specifically configured to:
s1, preprocessing the data;
aiming at the remote sensing data of each time phase, carrying out cloud detection on each remote sensing image of the current time phase, and eliminating an invalid value of the cloud; splicing the remote sensing images which can not completely cover the target area to obtain the remote sensing images which can completely cover the target area; averaging a plurality of remote sensing images capable of completely covering a target area to serve as a remote sensing image of the current time phase;
cutting the remote sensing image of the current time phase by using the vector data of the target area to obtain the remote sensing image of the current time phase of the target area;
s2, determining remote sensing index thresholds corresponding to the remote sensing indexes of the land use types;
calculating the remote sensing index of each pixel in the remote sensing image of the current time phase of the target area;
obtaining the land use type of each pixel according to the land use type classification result of the current time phase of the target area;
counting to obtain a remote sensing index threshold value of each land utilization type corresponding to the current time according to the remote sensing index and the land utilization type of each pixel;
and processing the remote sensing data and the land use type classification results of at least two time phases in the historical time period based on the steps S1 and S2 to obtain the remote sensing index threshold value of each land use type corresponding to each time phase.
In one possible implementation, the remote sensing index includes: one or more of a normalized vegetation index NDVI, a normalized architectural index NDBI, and a normalized water body index NDWI;
NDVI＝(NIR-R)/(NIR+R)；
NDBI＝(MIR-NIR)/(MIR+NIR)；
NDWI＝(G-NIR)/(G+NIR)；
wherein G, R, NIR and MIR respectively represent green, red, near red and mid-red wave bands.
In a possible implementation manner, the classification module is specifically configured to:
preprocessing the remote sensing data of at least two time phases in the current time period to obtain remote sensing images of all the time phases in the current time period of a target area;
aiming at the remote sensing image of each time phase in the current time period, identifying the land utilization type of each pixel by adopting a remote sensing index threshold corresponding to the time phase;
if the land utilization types of the same pixel at different time phases are the same, classifying the pixel into the land utilization type; and by analogy, a land use type classification result of the target area in the current time period is obtained.
In one possible implementation, the land use types include one or more of a body of water, a field, a shrub, a landscape site, and an artificial surface.
In one possible implementation, the remote sensing data is L1C data of a Sentinel-2B satellite.
A third aspect of the present application provides an electronic device comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of the first aspect of the application when executing the computer program.
A fourth aspect of the present application provides a computer readable storage medium having computer readable instructions stored thereon which are executable by a processor to implement the method of the first aspect of the present application.
Compared with the prior art, the method, the device, the electronic equipment and the storage medium for classifying the land use types, provided by the embodiment of the application, are used for acquiring remote sensing data and land use type classification results of at least two time phases of a target area in a historical time period; according to the remote sensing data and the land use type classification results of at least two time phases in the historical time period, counting to obtain a remote sensing index threshold value of each land use type corresponding to each time phase; acquiring remote sensing data of at least two time phases of a target area in a current time period; wherein the at least two phases in the historical period comprise at least two phases in the current period; and obtaining a land use type classification result of the target area in the current time period according to the remote sensing data of at least two time phases in the current time period and the remote sensing index threshold corresponding to each time phase. According to the technical scheme, high-reliability land use classification data in a historical time period is used as a basis, different time-phase multi-period remote sensing data are combined, threshold ranges of different time-phase and different land feature type remote sensing indexes are obtained, the threshold ranges are applied to new remote sensing images, new land use type classification results are obtained, classification accuracy of land use types can be improved, reliability is high, and the method has important significance in monitoring land use conditions.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a flowchart illustrating a method for classifying land use types according to the present application;
FIG. 2 is a flow chart illustrating a specific land use type classification method provided by the present application;
FIG. 3 shows a schematic view of a land use type sorting device provided by the present application;
FIG. 4 illustrates a schematic diagram of an electronic device provided herein;
FIG. 5 illustrates a schematic diagram of a computer-readable storage medium provided herein.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which this application belongs.
In addition, the terms "first" and "second", etc. are used to distinguish different objects, rather than to describe a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
In recent years, land utilization and vegetation information have become important contents for the research of modern socioeconomic and environmental sustainable development, and are also the link between human socioeconomic activities and natural ecological processes. Land utilization enables people to conduct management and management on land in a periodic mode according to characteristics of the land, and meanwhile land utilization is also one of hot problems for researching global changes.
The remote sensing image has the advantages of wide coverage range, low cost, strong timeliness and the like, and becomes an important data source for obtaining land utilization types. However, it is always the problem that researchers are focused on solving how to accurately obtain land use conditions, effectively identify land features, quickly obtain land feature distribution conditions and classify land use types from remote sensing images with different spatial resolutions. With the continuous development of medium and high spatial resolution remote sensing images, the efficient, accurate and timely management of the acquisition condition of land utilization types has become a mainstream trend.
The remote sensing image represents the difference of different ground features through the difference of brightness values or pixel values (spectral information of the ground features) and the spatial variation (spatial information of the ground features), which is the basis for distinguishing the different ground features in the image. The remote sensing image classification is to analyze the spectral information and the spatial information of all ground objects in the remote sensing image by using a computer, select characteristics, divide each pixel in the image into different categories according to a certain rule or algorithm, and then obtain information corresponding to the actual ground objects in the remote sensing image, thereby realizing the classification of the remote sensing image. The classification method of the remote sensing image can be divided into visual interpretation and computer automatic classification.
Visual interpretation mainly uses human experience and knowledge to interpret marks according to textures, shapes, shades, colors and the like, and refers to some non-remote sensing information to obtain the geoscience information of the image. The visual interpretation method is generally high in precision and mature, and is still applied to some projects and researches till now, but due to the defects of large manual investment, low efficiency, high interpretation experience requirement and the like of the visual interpretation, the visual interpretation method is difficult to be applied to processing of mass remote sensing data.
With the continuous development of information technology, computer technology and remote sensing technology, the classification method of remote sensing images has been changed from initial manual visual interpretation to automatic classification, and the computer automatic classification technology has gradually become the main research direction of remote sensing technology and application. In recent years, researchers at home and abroad make a lot of research in the field of remote sensing digital image processing, and besides traditional supervised unsupervised algorithms such as maximum likelihood, bayesian learning and clustering methods, a plurality of nonlinear intelligent algorithms are also applied to remote sensing image classification such as artificial neural networks, support vector machines, decision trees and the like. However, the remote sensing image is a complex earth surface information, and the classification quality of the remote sensing image is often influenced by many factors such as selection of the remote sensing image, a data preprocessing method and the like, so that the accurate classification of the remote sensing image still faces a huge challenge.
At present, many scholars use high-resolution satellite data, process, fuse and combine the satellite data with ground actual measurement verification data to produce high-resolution land use type data covering the whole country. However, most studies use single-phase remote sensing images as data sources, image expression of ground feature types is not taken into consideration along with seasonal changes, and time phase information of the images is ignored.
In view of the above, the present application provides a method, an apparatus, a device and a storage medium for classifying land utilization types. To further illustrate aspects of embodiments of the present application, reference is made to the following description taken in conjunction with the accompanying drawings. It is to be understood that, in the following embodiments, the same or corresponding contents may be mutually referred, and for simplicity and convenience of description, the subsequent description is not repeated.
Referring to fig. 1, a flowchart of a method for classifying land utilization types according to an embodiment of the present application is shown, where an execution subject of the method may be a server, or an electronic device such as a mobile phone or a computer. As shown in fig. 1, the method for classifying the land use types may include the following steps S101 to S103:
s101, remote sensing data and land use type classification results of at least two time phases of a target area in a historical time period are obtained.
The remote sensing data can be optical remote sensing images, such as MODIS, Landsat, Sentinel-2/3 and other satellite remote sensing images, and images of the earth surface are obtained by using visible light, infrared and other wave bands, and images similar to photographs are obtained. Optionally, the remote sensing data of the present application may be L1C data of a Sentinel-2B satellite.
Things change regularly over time, called time phases. In this embodiment, the at least two time phases within the historical period may refer to at least two seasons of the year. For example, the telemetry data for at least two phases over the historical period may be the Sentinel-2B telemetry data for months 1 and 6 in 2015.
The land use type classification result obtained in the step can be GLC _ FCS30-2015, which is a multi-time-phase classification method based on random forests and fully combines time series MCD43A4 NBAR (Germinal bidirectional reflectance distribution function adjusted) products and CCI _ LC land cover products, and annual land use classification data containing 30 land cover types are generated. The product (GLC _ FCS30-2015) has important reference and reference significance and can be used as initial underlying background data. GLC _ FCS30-2015 land use classification data are used as initial land use classification true values in the application, so that the complex processes of visual interpretation and field investigation are reduced, and the reliability of background data is guaranteed.
The L1C data of the Sentinel-2B used in this embodiment can be selected by region and cloud cover on the relevant website to be screened and downloaded. The 2015 land use classification data is also downloaded from the related data sharing service system.
S102, according to the remote sensing data and the land use type classification results of at least two time phases in the historical time period, counting to obtain a remote sensing index threshold value of each land use type corresponding to each time phase.
Specifically, step S102 can be implemented as follows:
the first step is as follows: preprocessing the data, wherein the specific process of data preprocessing is as follows:
aiming at the remote sensing data of each time phase, carrying out cloud detection on each remote sensing image of the current time phase, and removing an invalid value of the cloud; splicing the remote sensing images which cannot completely cover the target area to obtain the remote sensing images which can completely cover the target area; averaging a plurality of remote sensing images capable of completely covering a target area to serve as a remote sensing image of the current time phase;
and cutting the remote sensing image of the current time phase by using the vector data of the target area to obtain the remote sensing image of the current time phase of the target area.
The second step is that: determining a remote sensing index threshold value corresponding to the remote sensing index of each land use type, wherein the specific process is as follows:
calculating the remote sensing index of each pixel in the remote sensing image of the current time phase of the target area;
obtaining the land use type of each pixel according to the land use type classification result of the current time phase of the target area;
counting to obtain a remote sensing index threshold value of each land utilization type corresponding to the current time according to the remote sensing index and the land utilization type of each pixel;
and processing the remote sensing data and the land use type classification results of at least two time phases in the historical time period based on the steps S1 and S2 to obtain the remote sensing index threshold value of each land use type corresponding to each time phase.
The following illustrates the acquisition process of the remote sensing index threshold value of the land use type.
The L1C data of the Sentinel-2B is an atmospheric apparent reflectivity product subjected to orthorectification and geometric fine correction, atmospheric correction is not carried out, a related Sen2cor plug-in is used for carrying out atmospheric correction and resampling on L1C, the spatial resolution is resampled to be 10m, then all wave bands are subjected to wave band combination, and finally a Sentinel-2B remote sensing image with the resolution of 10m is obtained, and compared with the current common resolution of 30m, the resolution is higher. The remote sensing images which cannot completely cover the target area are spliced, cloud detection is carried out on each period of image, an invalid value of cloud is eliminated, and then the average value of a plurality of images in one month is used as the remote sensing image in the month, so that the stability of data is ensured. And finally, cutting the remote sensing image of the month by using the vector of the target area.
The remote sensing index may include one or more of a normalized vegetation index NDVI, a normalized construction index NDBI, and a normalized water body index NDWI. The normalization index is a normalization method which finds out the strongest reflection wave band and the weakest reflection wave band of the ground class to be researched to carry out ratio operation according to the characteristics of various ground object types in the multispectral wave band, so that the brightness of the ground object to be researched is enhanced, the brightness of the other ground object types is weakened, and the wave band brightness is between-1 and 1.
a. Normalized vegetation index NDVI ═ (NIR-R)/(NIR + R)
NDVI is the best indicator of vegetation coverage and vegetation growth status, and can reflect changes in seasons and human activities.
b. Normalized building index NDBI ═ (MIR-NIR)/(MIR + NIR)
Because most of the construction land is impervious, the reflectivity of the middle infrared band is higher than that of the near infrared band in most of the construction land, but the difference between the two bands is far smaller than that of each band in the vegetation index, and the construction land is influenced by the types of land features with similar characteristics of other bands, so that the construction land is difficult to be extracted by only depending on NDBI when the types of the land features are complex and is often combined with other indexes.
c. Normalized water index NDWI ═ G-NIR)/(G + NIR)
The water body almost completely absorbs the light waves with the wavelengths in the near infrared and middle infrared wavelength ranges, and the reflectivity of the light waves with the wavelengths in the visible light to middle infrared wavelength ranges is gradually weakened, so that the water body information in the remote sensing image can be quickly extracted by using the NDWI.
Wherein G, R, NIR and MIR respectively represent green, red, near red and mid-red wave bands.
And classifying and combining the downloaded 2015-year land utilization types, determining the land utilization types to be five types of water bodies, cultivated lands, shrubs, garden lands and artificial surfaces, and cutting classification data by using a target area vector. Since the land use type data resolution is 30m, in order to keep the same resolution as the Sentinel-2B data, the clipped classified data is resampled to 10 m.
According to different types of numerical values of land utilization type data in 2015, remote sensing normalization indexes of 1 month and 6 months of the penta-land utilization type are respectively extracted, and a value range of the remote sensing normalization indexes is calculated.
Taking farmland utilization type extraction as an example, for data of 1 month in 2015, the value range of a farmland normalized vegetation index is [0.48, 0.62], the value range of a normalized building index is [ -0.22, 0.16], and the value range of a normalized water body index is [ -0.79, -0.56 ]; for data of month 6 of 2015, the value range of the arable normalized vegetation index is [0.16, 0.28], the value range of the normalized construction index is [ -0.19, 0.23], and the value range of the normalized water body index is [ -0.48, -0.25 ]. Most of cultivated land in the target area is planted with winter wheat, and the winter wheat is harvested in 6 months, so that three remote sensing normalization index changes of the cultivated land are influenced.
S103, obtaining remote sensing data of at least two time phases of the target area in the current time period.
Wherein the at least two phases within the history period include the at least two phases within the current period, that is, the phase change of the current period is included within the phase change of the history period. In this embodiment, the Sentinel-2B remote sensing data of months 1 and 6 in 2020 is obtained.
And S104, obtaining a land use type classification result of the target area in the current time period according to the remote sensing data of at least two time phases in the current time period and the remote sensing index threshold corresponding to each time phase.
Specifically, step S104 specifically includes:
preprocessing the remote sensing data of at least two time phases in the current time period to obtain remote sensing images of all the time phases in the current time period of a target area; the preprocessing method is the same as the preprocessing process of the remote sensing data in the historical time period, and is not described herein again.
And aiming at the remote sensing image of each time phase in the current time period, identifying the land utilization type of each pixel by adopting a remote sensing index threshold corresponding to the time phase. The pixel refers to the minimum unit for scanning and sampling the ground scenery by the sensor during remote sensing data acquisition, such as scanning imaging.
If the land utilization types of the same pixel at different time phases are the same, classifying the pixel into the land utilization type; and by analogy, the land use type classification result of the target area in the current time period is obtained.
For example, the remote sensing index threshold of the month 1 and the month 6 in 2015 obtained in step S102 is applied to the month 1 and the month 6 in 2020 to perform 3 remote sensing normalized index judgments, for example, the normalized vegetation index of a certain pixel month 1 is 0.54, the normalized building index is-0.18, and the normalized water body index is-0.68; and the normalized vegetation index in month 6 is 0.25, the normalized construction index is 0.18, the normalized water body index is-0.36, and if the normalized vegetation index meets the threshold range of cultivated land, the pixel is divided into cultivated land. Referring to the above process, classification of the land use type of the target region 2020 is completed.
For ease of understanding, fig. 2 of the present application shows a flowchart of a specific land use type classification method. The Sentinel 2 data refers to Sentinel-2B remote sensing data. According to the method, Sentinel-2B remote sensing data of different time phases of the historical time period of the target area are combined, multiple remote sensing image normalization indexes are calculated by utilizing the high spatial resolution of 10 meters and the characteristic of sensitive and rich wave band information, the threshold ranges corresponding to different land use types are comprehensively judged, the rules are applied to the Sentinel-2B remote sensing data of the time phase corresponding to the current time period, and the land use type classification result of the current time period of the target area is obtained.
In the embodiment, the classification result of the land use type in the current period is obtained by calculation according to different rules of the remote sensing normalization index value domains corresponding to different ground objects by taking the historical period land use type data as the basis and combining multi-time-phase high-resolution remote sensing data.
In the above embodiment, a method for classifying land use types is provided, and correspondingly, a device for classifying land use types is also provided. Since the apparatus embodiments are substantially similar to the method embodiments, they are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for relevant points. The device embodiments described below are merely illustrative.
As shown in fig. 3, the land use type classification device 10 may include:
the acquisition module 101 is used for acquiring remote sensing data and land use type classification results of at least two time phases of a target area in a historical time period;
the statistical module 102 is used for obtaining a remote sensing index threshold value of each land utilization type corresponding to each time phase through statistics according to the remote sensing data and the land utilization type classification results of at least two time phases in the historical time period;
the acquisition module 101 is further configured to acquire remote sensing data of at least two time phases of the target area in the current time period; wherein the at least two phases in the historical period comprise at least two phases in the current period;
the classification module 103 is configured to obtain a land use type classification result of the target area in the current time period according to the remote sensing data of the at least two time phases in the current time period and the remote sensing index threshold corresponding to each time phase.
In a possible implementation manner, the statistical module is specifically configured to:
s1, preprocessing the data;
aiming at the remote sensing data of each time phase, carrying out cloud detection on each remote sensing image of the current time phase, and eliminating an invalid value of the cloud; splicing the remote sensing images which cannot completely cover the target area to obtain the remote sensing images which can completely cover the target area; averaging a plurality of remote sensing images capable of completely covering a target area to serve as a remote sensing image of the current time phase;
cutting the remote sensing image of the current time phase by using the vector data of the target area to obtain the remote sensing image of the current time phase of the target area;
s2, determining remote sensing index thresholds corresponding to the remote sensing indexes of the land use types;
calculating the remote sensing index of each pixel in the remote sensing image of the current time phase of the target area;
obtaining the land use type of each pixel according to the land use type classification result of the current time phase of the target area;
counting to obtain a remote sensing index threshold value of each land utilization type corresponding to the current time according to the remote sensing index and the land utilization type of each pixel;
and processing the remote sensing data and the land use type classification results of at least two time phases in the historical time period based on the steps S1 and S2 to obtain the remote sensing index threshold value of each land use type corresponding to each time phase.
In one possible implementation, the remote sensing index includes: one or more of a normalized vegetation index NDVI, a normalized construction index NDBI, and a normalized water body index NDWI;
NDVI＝(NIR-R)/(NIR+R)；
NDBI＝(MIR-NIR)/(MIR+NIR)；
NDWI＝(G-NIR)/(G+NIR)；
wherein G, R, NIR and MIR respectively represent green, red, near red and mid-red wave bands.
In a possible implementation manner, the classification module is specifically configured to:
preprocessing the remote sensing data of at least two time phases in the current time period to obtain remote sensing images of all the time phases in the current time period of a target area;
aiming at the remote sensing image of each time phase in the current time period, adopting a remote sensing index threshold value corresponding to the time phase to identify the land utilization type of each pixel;
if the land utilization types of the same pixel at different time phases are the same, classifying the pixel into the land utilization type; and by analogy, the land use type classification result of the target area in the current time period is obtained.
In one possible implementation, the land use types include one or more of a body of water, a field, a shrub, a landscape site, and an artificial surface.
In one possible implementation, the remote sensing data is L1C data of a Sentinel-2B satellite.
The classification device of the land use types provided by the embodiment of the application and the classification method of the land use types provided by the embodiment of the application have the same beneficial effects as the adopted, operated or realized method.
The present application further provides an electronic device, such as a mobile phone, a laptop, a tablet computer, a desktop computer, etc., corresponding to the method for classifying land use types provided in the foregoing embodiments, so as to execute the method for classifying land use types.
Referring to fig. 4, a schematic diagram of an electronic device provided in some embodiments of the present application is shown. As shown in fig. 4, the electronic device 20 includes: the system comprises a processor 200, a memory 201, a bus 202 and a communication interface 203, wherein the processor 200, the communication interface 203 and the memory 201 are connected through the bus 202; the memory 201 stores a computer program that can be executed on the processor 200, and the processor 200 executes the computer program to execute the land use type classification method provided by any one of the foregoing embodiments of the present application.
The Memory 201 may include a high-speed Random Access Memory (RAM) and may further include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 203 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.
The processor 200 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 200. The Processor 200 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 201, and the processor 200 reads the information in the memory 201 and completes the steps of the method in combination with the hardware thereof.
The electronic equipment provided by the embodiment of the application and the land use type classification method provided by the embodiment of the application have the same beneficial effects as the method adopted, operated or realized by the electronic equipment.
The present embodiment also provides a computer readable storage medium corresponding to the classification method of land use type provided by the foregoing embodiment, please refer to fig. 5, which shows a computer readable storage medium as an optical disc 30, on which a computer program (i.e. a program product) is stored, wherein the computer program, when executed by a processor, executes the classification method of land use type provided by any of the foregoing embodiments.
It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory, or other optical and magnetic storage media, which are not described in detail herein.
The computer-readable storage medium provided by the above-mentioned embodiment of the present application and the land use type classification method provided by the embodiment of the present application have the same beneficial effects as the method adopted, operated or implemented by the application program stored in the computer-readable storage medium.
It should be noted that the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
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.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and 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 units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present disclosure, and the present disclosure should be construed as being covered by the claims and the specification.
Claims (10)
1. A method for classifying types of land use, comprising:
obtaining remote sensing data and land use type classification results of at least two time phases of a target area in a historical time period;
according to the remote sensing data and the land use type classification results of at least two time phases in the historical time period, counting to obtain a remote sensing index threshold value of each land use type corresponding to each time phase;
acquiring remote sensing data of at least two time phases of a target area in a current time period; wherein the at least two time phases in the historical time period comprise at least two time phases in the current time period;
and obtaining a land use type classification result of the target area in the current time period according to the remote sensing data of at least two time phases in the current time period and the remote sensing index threshold corresponding to each time phase.
2. The method according to claim 1, wherein the step of obtaining the remote sensing index threshold value of each land utilization type corresponding to each time phase in a statistical manner according to the remote sensing data of at least two time phases in the historical period and the land utilization type classification result comprises the following steps:
s1, preprocessing the data;
aiming at the remote sensing data of each time phase, carrying out cloud detection on each remote sensing image of the current time phase, and eliminating an invalid value of the cloud; splicing the remote sensing images which cannot completely cover the target area to obtain the remote sensing images which can completely cover the target area; averaging a plurality of remote sensing images capable of completely covering a target area to serve as a remote sensing image of the current time phase;
cutting the remote sensing image of the current time phase by using the vector data of the target area to obtain the remote sensing image of the current time phase of the target area;
s2, determining remote sensing index thresholds corresponding to the remote sensing indexes of the land use types;
calculating the remote sensing index of each pixel in the remote sensing image of the current time phase of the target area;
obtaining the land use type of each pixel according to the land use type classification result of the current time phase of the target area;
counting to obtain a remote sensing index threshold value of each land utilization type corresponding to the current time according to the remote sensing index and the land utilization type of each pixel;
and processing the remote sensing data and the land use type classification results of at least two time phases in the historical time period based on the steps S1 and S2 to obtain the remote sensing index threshold value of each land use type corresponding to each time phase.
3. The method of claim 2, wherein the remote sensing index comprises: one or more of a normalized vegetation index NDVI, a normalized construction index NDBI, and a normalized water body index NDWI;
NDVI＝(NIR-R)/(NIR+R)；
NDBI＝(MIR-NIR)/(MIR+NIR)；
NDWI＝(G-NIR)/(G+NIR)；
wherein G, R, NIR and MIR respectively represent green, red, near red and mid-red wave bands.
4. The method according to claim 2, wherein the step of obtaining a land use type classification result of the target area in the current time period according to the remote sensing data of at least two time phases in the current time period and the remote sensing index threshold corresponding to each time phase comprises the following steps:
preprocessing the remote sensing data of at least two time phases in the current time period to obtain remote sensing images of all the time phases in the current time period of a target area;
aiming at the remote sensing image of each time phase in the current time period, adopting a remote sensing index threshold value corresponding to the time phase to identify the land utilization type of each pixel;
if the land utilization types of the same pixel at different time phases are the same, classifying the pixel into the land utilization type; and by analogy, the land use type classification result of the target area in the current time period is obtained.
5. The method of claim 1, wherein the land use types include one or more of bodies of water, arable land, bushes, garden land, and artificial surfaces.
6. The method of claim 1, wherein the telemetry data is L1C data from a Sentinel-2B satellite.
7. A sorting device of the type of land use, characterized in that it comprises:
the acquisition module is used for acquiring remote sensing data and land utilization type classification results of at least two time phases of a target area in a historical time period;
the statistical module is used for obtaining remote sensing index threshold values of all land utilization types corresponding to each time phase through statistics according to the remote sensing data and the land utilization type classification results of at least two time phases in the historical time period;
the acquisition module is further used for acquiring remote sensing data of at least two time phases of the target area in the current time period; wherein the at least two phases in the historical period comprise at least two phases in the current period;
and the classification module is used for obtaining a land utilization type classification result of the target area in the current time period according to the remote sensing data of at least two time phases in the current time period and the remote sensing index threshold corresponding to each time phase.
8. The apparatus of claim 7, wherein the statistics module is specifically configured to:
s1, preprocessing the data;
aiming at the remote sensing data of each time phase, carrying out cloud detection on each remote sensing image of the current time phase, and eliminating an invalid value of the cloud; splicing the remote sensing images which cannot completely cover the target area to obtain the remote sensing images which can completely cover the target area; averaging a plurality of remote sensing images capable of completely covering a target area to serve as a remote sensing image of the current time phase;
cutting the remote sensing image of the current time phase by using the vector data of the target area to obtain the remote sensing image of the current time phase of the target area;
s2, determining remote sensing index thresholds corresponding to the remote sensing indexes of the land use types;
calculating the remote sensing index of each pixel in the remote sensing image of the current time phase of the target area;
obtaining the land use type of each pixel according to the land use type classification result of the current time phase of the target area;
counting to obtain a remote sensing index threshold value of each land utilization type corresponding to the current time according to the remote sensing index and the land utilization type of each pixel;
and processing the remote sensing data and the land use type classification results of at least two time phases in the historical time period based on the steps S1 and S2 to obtain the remote sensing index threshold value of each land use type corresponding to each time phase.
9. An electronic device, comprising: memory, processor and computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 6 when executing the computer program.
10. A computer readable storage medium having computer readable instructions stored thereon which are executable by a processor to implement the method of any one of claims 1 to 6.
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