Surface type detection method, device and equipment based on UAV remote sensing images

Abstract

The present invention relates to the field of remote sensing data analysis, and in particular to a method for detecting land surface types based on remote sensing images of unmanned aerial vehicles, the method comprising: obtaining a remote sensing image data set and a temperature data set of a target area through a unmanned aerial vehicle, wherein the remote sensing image data set includes a plurality of thermal infrared image data, and the temperature data set includes air temperature parameters corresponding to the thermal infrared image data; performing mask processing on the thermal infrared image data to obtain impermeable surface image data; obtaining a sensitivity index of thermal radiation characteristics of each pixel of the impermeable surface image data of the target area according to the impermeable surface image data and the temperature data set, and constructing a sensitivity index data set of thermal radiation characteristics; obtaining land surface type classification data of the target area according to the sensitivity index data set of thermal radiation characteristics. By combining thermal radiation characteristics, the land surface type is classified, and the surface temperature of the land surface type is accurately reflected, thereby accurately reflecting the thermal environment of the current area, which is efficient and fast.

Description

Technical Field

[0001] The present invention relates to the field of remote sensing data analysis, and in particular to a method, device, equipment and storage medium for detecting land surface types based on unmanned aerial vehicle remote sensing images. Background Art

[0002] The urban thermal environment in a small area can have a significant impact on human health and the urban heat island effect. By analyzing the differences in surface types, the surface temperature of a small area can be accurately measured, thereby reflecting the thermal environment of the current area.

[0003] The current technical solution is to classify the surface types based on the material of the surface type by obtaining the spectral characteristics of the surface types with different materials. However, the temperature resistance of different surface types cannot be reflected only based on the differences in their spectral characteristics, and the surface temperature of the surface type cannot be accurately reflected, and thus the thermal environment of the current area cannot be accurately reflected. Summary of the invention

[0004] Based on this, the purpose of the present invention is to provide a surface type detection method, device, equipment and storage medium based on UAV remote sensing images, by processing the thermal infrared image of the target area, obtaining impermeable surface image data, analyzing the thermal radiation characteristics of the impermeable surface image data, and constructing a sensitivity index data set of thermal radiation characteristics. According to the sensitivity index data set of thermal radiation characteristics, the surface type of the target area is classified. By combining the thermal radiation characteristics, the surface types are classified to reflect the temperature resistance of different surface types, accurately reflect the surface temperature of the surface type, and thus accurately reflect the thermal environment of the current area, efficiently and quickly.

[0005] In a first aspect, an embodiment of the present application provides a method for detecting surface types based on drone remote sensing images, comprising the following steps:

[0006] Acquire a remote sensing image data set and a temperature data set of a target area by using a drone, wherein the remote sensing image data set includes a plurality of thermal infrared image data, and the temperature data set includes air temperature parameters corresponding to the thermal infrared image data;

[0007] Performing mask processing on the thermal infrared image data to obtain impermeable surface image data;

[0008] According to the impervious surface image data and the temperature data set, a sensitivity index of the thermal radiation feature of each pixel of the impervious surface image data of the target area is obtained to construct a sensitivity index data set of the thermal radiation feature;

[0009] Acquiring the surface type classification data of the target area according to the sensitivity index data set of the thermal radiation characteristics;

[0010] In response to a detection instruction, the detection instruction includes a remote sensing image data set and a temperature data set of the area to be detected, obtaining electronic map data, obtaining surface type classification data of the area to be detected based on the remote sensing image data set and the temperature data set of the area to be detected, obtaining surface identifiers of various areas of the electronic map data based on the surface types in the surface type classification data and the surface identifiers corresponding to the surface types, and displaying and marking the surface identifiers on the electronic map data.

[0011] In a second aspect, an embodiment of the present application provides a surface type detection device based on drone remote sensing images, comprising:

[0012] An acquisition module, used to acquire a remote sensing image data set and a temperature data set of a target area through a UAV, wherein the remote sensing image data set includes a plurality of thermal infrared image data, and the temperature data set includes air temperature parameters corresponding to the thermal infrared image data;

[0013] A processing module, used for performing mask processing on the thermal infrared image data to obtain impermeable surface image data;

[0014] A construction module, used to obtain the sensitivity index of the thermal radiation characteristics of each pixel of the impervious surface image data of the target area according to the impervious surface image data and the temperature data set, and to construct a sensitivity index data set of the thermal radiation characteristics;

[0015] A classification module, used for obtaining the surface type classification data of the target area according to the sensitivity index data set of the thermal radiation characteristics;

[0016] A detection module is used to respond to a detection instruction, wherein the detection instruction includes a remote sensing image data set and a temperature data set of an area to be detected, obtain electronic map data, obtain surface type classification data of the area to be detected based on the remote sensing image data set and the temperature data set of the area to be detected, obtain surface identification of each area of ​​the electronic map data based on the surface type in the surface type classification data and the surface identification corresponding to the surface type, and display and mark the surface identification on the electronic map data.

[0017] In a third aspect, an embodiment of the present application provides a computer device comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor; when the computer program is executed by the processor, the steps of the surface type detection method based on drone remote sensing images as described in the first aspect are implemented.

[0018] In a fourth aspect, an embodiment of the present application provides a storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the surface type detection method based on drone remote sensing images as described in the first aspect.

[0019] In an embodiment of the present application, a method, device, equipment and storage medium for detecting surface types based on unmanned aerial vehicle remote sensing images are provided. By processing the thermal infrared image of the target area, impermeable surface image data is obtained, the thermal radiation characteristics of the impermeable surface image data are analyzed, and a sensitivity index data set of thermal radiation characteristics is constructed. According to the sensitivity index data set of thermal radiation characteristics, the surface type of the target area is classified. By combining the thermal radiation characteristics, the surface types are classified, reflecting the temperature resistance performance of different surface types, accurately reflecting the surface temperature of the surface type, and thus accurately reflecting the thermal environment of the current area, which is efficient and fast.

[0020] For better understanding and implementation, the present invention is described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A schematic diagram of a flow chart of a method for detecting land surface types based on UAV remote sensing images provided in one embodiment of the present application;

[0022] Figure 2 A schematic diagram of the process of S2 in a method for detecting land surface types based on drone remote sensing images provided in one embodiment of the present application;

[0023] Figure 3 A schematic diagram of the flow of S201 in a method for detecting land surface types based on drone remote sensing images provided in one embodiment of the present application;

[0024] Figure 4 A schematic diagram of the process of S4 in a method for detecting land surface types based on drone remote sensing images provided in one embodiment of the present application;

[0025] Figure 5 A schematic diagram of a flow chart of a method for detecting land surface types based on drone remote sensing images provided in another embodiment of the present application;

[0026] Figure 6 A schematic diagram of the structure of a surface type detection device based on UAV remote sensing images provided in one embodiment of the present application;

[0027] Figure 7 A schematic diagram of the structure of a device provided for one embodiment of the present application. DETAILED DESCRIPTION

[0028] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0029] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0030] It should be understood that, although the terms first, second, third, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" / "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determination".

[0031] See also Figure 1 , Figure 1 A schematic flow chart of a method for detecting land surface types based on drone remote sensing images provided in one embodiment of the present application, the method comprising the following steps:

[0032] S1: Acquire a remote sensing image dataset and a temperature dataset of a target area through a drone, wherein the remote sensing image dataset includes a plurality of thermal infrared image data, and the temperature dataset includes air temperature parameters corresponding to the thermal infrared image data.

[0033] The executor of the surface type detection method based on UAV remote sensing images is a detection device of the surface type detection method based on UAV remote sensing images (hereinafter referred to as the detection device). In an optional embodiment, the detection device can be a computer device, a server, or a server cluster composed of multiple computer devices.

[0034] The target area includes several land types, wherein the land types include a strong temperature-resistance land feature type, a medium temperature-resistance land feature type, a sub-weak temperature-resistance land feature type, and a weak temperature-resistance land feature type.

[0035] The thermal infrared image data is used to record thermal infrared radiation information radiated by ground objects that is invisible to the human eye. The detection equipment can obtain the remote sensing image data set of the target area through a drone, or by downloading it from a database.

[0036] The temperature data set includes meteorological parameters and air temperature parameters, wherein the meteorological parameters include atmospheric transmittance and surface emissivity, and the air temperature parameters include the local temperature when the thermal infrared image data is acquired, the highest temperature of the day, and the lowest temperature. The detection device can obtain the temperature data set by downloading it from the database

[0037] S2: performing mask processing on the thermal infrared image data to obtain impermeable surface image data.

[0038] In the field of remote sensing, vegetation, water and soil indices have been widely defined and applied. According to the characteristics of vegetation, water and soil, satellite bands are combined and contrast is calculated to highlight vegetation, water and soil. Through mask operation, the vegetation, water and soil types are filtered, and the filtered image data is the impervious surface image data.

[0039] In this embodiment, the detection device performs mask processing on the thermal infrared image data to obtain impermeable surface image data.

[0040] See also Figure 2 , Figure 2 The schematic flow diagram of S2 in the surface type detection method based on drone remote sensing images provided in one embodiment of the present application includes steps S201 to S202, which are specifically as follows:

[0041] S201: Obtain mask data corresponding to the thermal infrared image data.

[0042] The mask data includes vegetation mask data, water body mask data and soil mask data.

[0043] In this embodiment, the detection device obtains vegetation mask data, water mask data and soil mask data based on the thermal infrared image data and the corresponding index algorithm, wherein the index algorithm includes a normalized vegetation index algorithm, a normalized difference water index algorithm and a soil adjusted vegetation index algorithm.

[0044] See also Figure 3 , Figure 3 The schematic flow diagram of S2 in the surface type detection method based on UAV remote sensing images provided in one embodiment of the present application includes steps S2011 to S2013, which are specifically as follows:

[0045] S2011: According to the thermal infrared image data and the normalized vegetation index algorithm, obtain the normalized vegetation index of the thermal infrared image data; according to the normalized vegetation index and a preset normalized vegetation index threshold, extract vegetation area image data from the thermal infrared image data; according to the vegetation area image data, obtain vegetation mask data.

[0046] The normalized difference vegetation index algorithm is:

[0047]

[0048] Wherein, NDVI is the normalized vegetation index, NIR is the near infrared band, and R is the infrared band;

[0049] The detection device obtains the near infrared band NIR and the infrared band R corresponding to the thermal infrared image data according to the thermal infrared image data, inputs them into a preset normalized vegetation index algorithm, and obtains the normalized vegetation index of each pixel of the thermal infrared image data.

[0050] In an optional embodiment, the detection device sets the normalized vegetation index threshold to 0.9, compares the acquired normalized vegetation index with the normalized vegetation index threshold, extracts pixels whose normalized vegetation index is greater than the normalized vegetation index threshold, combines them, obtains a binary map of vegetation distribution in the target area, and generates a vegetation mask file based on the vegetation distribution map.

[0051] S2012: According to the thermal infrared image data and the normalized difference water index algorithm, obtain the normalized difference water index of the thermal infrared image data; according to the normalized difference water index and a preset normalized difference water index threshold, extract water body area image data from the thermal infrared image data; according to the water body area image data, obtain water body mask data.

[0052] The normalized difference water index algorithm is:

[0053]

[0054] Wherein, MNDWI is the normalized difference water index, Green is the green band, and MIR is the mid-infrared band;

[0055] The detection device obtains the green band Green and the mid-infrared band MIR corresponding to the thermal infrared image data according to the thermal infrared image data, and inputs them into a preset normalized difference water index algorithm to obtain the normalized difference water index of each pixel of the thermal infrared image data.

[0056] In an optional embodiment, the detection device sets the normalized difference water index threshold to 0.9, compares the acquired normalized difference water index with the normalized difference water index threshold, extracts pixels whose normalized difference water index is greater than the normalized difference water index threshold, combines them, obtains a binary map of water body distribution in the target area, and generates a water body mask file based on the water body distribution map.

[0057] S2013: According to the thermal infrared image data and the soil adjustment vegetation index algorithm, obtain the soil adjustment vegetation index of the thermal infrared image data; according to the soil adjustment vegetation index and a preset soil adjustment vegetation index threshold, extract soil area image data from the thermal infrared image data; according to the soil area image data, obtain soil mask data.

[0058] The soil adjusted vegetation index algorithm is:

[0059]

[0060] Wherein, MSAVI is the soil adjusted vegetation index, and Red is the red light band;

[0061] The detection device obtains the near infrared band NIR and the red light band Red corresponding to the thermal infrared image data according to the thermal infrared image data, inputs them into a preset soil adjustment vegetation index algorithm, and obtains the soil adjustment vegetation index of each pixel of the thermal infrared image data.

[0062] In an optional embodiment, the detection device sets the soil adjustment vegetation index threshold to 0.9, compares the acquired soil adjustment vegetation index with the soil adjustment vegetation index threshold, extracts pixels whose soil adjustment vegetation index is greater than the soil adjustment vegetation index threshold, combines them, obtains a binary map of soil distribution in the target area, and generates a soil mask file based on the soil distribution map.

[0063] S202: According to the mask data, filter the area corresponding to the mask data in the thermal infrared image data of the target area to obtain the impermeable surface image data.

[0064] The area corresponding to the mask data includes a vegetation area, a water area and a soil area.

[0065] In this embodiment, the detection device filters the vegetation, water body and soil areas in the thermal infrared image data according to the vegetation mask data, the water body mask data and the soil mask data to obtain the impermeable surface image data.

[0066] S3: According to the impervious surface image data and the temperature data set, a sensitivity index of the thermal radiation feature of each pixel of the impervious surface image data of the target area is obtained, and a sensitivity index data set of the thermal radiation feature is constructed.

[0067] In this embodiment, the detection device obtains the sensitivity index of the thermal radiation characteristics of each pixel of the impermeable surface image data of the target area based on the impermeable surface image data and the temperature data set, and combines the obtained sensitivity indexes of the thermal radiation characteristics to construct a sensitivity index data set of the thermal radiation characteristics.

[0068] See also Figure 4 , Figure 4 The schematic flow diagram of S3 in the surface type detection method based on drone remote sensing images provided in one embodiment of the present application includes steps S301 to S302, which are specifically as follows:

[0069] S301: Acquire the surface temperature data of the impermeable surface image according to the impermeable surface image data and the surface temperature algorithm, wherein the surface temperature algorithm is:

[0070]

[0071] Where, T s is the surface temperature; C is the brightness temperature; C” is the equivalent atmospheric average temperature; a and b are regression coefficients; α and β are intermediate coefficients, where α=e·r, β=(1-e)[1+e(1-r)], e is the atmospheric transmittance, and r is the surface emissivity;

[0072] The ground surface temperature data refers to the ground surface temperature in each area recorded in the impermeable surface image data.

[0073] The brightness temperature value can be measured by a sensor of a drone. In this embodiment, the detection device obtains the surface emissivity and atmospheric transmittance of each pixel in the impervious surface image data, inputs them into a preset surface temperature algorithm, calculates the surface temperature corresponding to each pixel according to the preset equivalent atmospheric average temperature and brightness temperature value, and obtains the surface temperature data of the impervious surface image.

[0074] S302: According to the surface temperature data and the sensitivity index algorithm of thermal radiation characteristics, the sensitivity index of thermal radiation characteristics of each pixel of the impervious surface image data of the target area is obtained to construct a sensitivity index data set of thermal radiation characteristics.

[0075] The sensitivity index algorithm of the thermal radiation feature is:

[0076] TCI=(T s -T a_c ) / (Ta_max -T a_min )

[0077] Where TCI is the sensitivity index of the thermal radiation feature, T a_c is the local temperature when the thermal infrared image data is acquired in the temperature parameter, T a_max is the highest temperature of the day when the thermal infrared image data is acquired in the temperature parameter, T a_min It is the lowest temperature of the day when the thermal infrared image data in the temperature parameter is obtained.

[0078] Thermal radiation is the phenomenon that an object radiates electromagnetic waves due to temperature. The thermal radiation sensitivity index TCI (Thermal characteristic index) refers to the sensitivity of the characteristics determined by their own properties during the reflection, scattering, transmission and emission of electromagnetic radiation. The larger the TCI value, the weaker the resistance of this land type to temperature changes; correspondingly, the smaller the TCI value, the stronger the resistance of this land type to temperature changes.

[0079] In this embodiment, the detection equipment inputs the acquired surface temperature data into the sensitivity index algorithm of the thermal radiation characteristics, obtains the sensitivity index of the thermal radiation characteristics of each pixel of the impermeable surface image data of the target area according to the local temperature when the thermal infrared image data is acquired, the highest temperature of the day and the lowest temperature in the temperature parameters, and constructs a sensitivity index data set of thermal radiation characteristics.

[0080] S4: Acquire the surface type classification data of the target area according to the sensitivity index data set of the thermal radiation characteristics.

[0081] In this embodiment, the detection device classifies the sensitivity index data set of the thermal radiation feature, obtains the sensitivity index classification result of the thermal radiation feature, and obtains the surface type classification data of the target area according to the sensitivity index classification result of the thermal radiation feature.

[0082] See also Figure 5 , Figure 5 The schematic flow diagram of S4 in the surface type detection method based on drone remote sensing images provided in one embodiment of the present application includes steps S401 to S402, which are specifically as follows:

[0083] S401: Obtain the squared deviation parameter of the sensitivity index data set of the thermal radiation feature, wherein the squared deviation parameter includes a first squared deviation parameter and a second squared deviation parameter, the first squared deviation parameter is the squared deviation of the mean of the sensitivity index of the thermal radiation feature, and the second squared deviation parameter is the squared deviation of the square of the mean of the sensitivity index of the thermal radiation feature.

[0084] In this embodiment, the detection device calculates the mean value of the sensitivity index of the thermal radiation feature and calculates the square sum of the deviations of the mean value of the sensitivity index of the thermal radiation feature as the first deviation sum method and parameters, which are as follows:

[0085]

[0086]

[0087] In the formula, x m is the TCI value corresponding to the mth pixel in the impervious surface data, m is the number of all pixels in the impervious surface data, is the mean value of the sensitivity index of the thermal radiation characteristics in the impervious surface data, is the sum of squared deviations of the mean value of the sensitivity index of the thermal radiation feature.

[0088] The detection device divides the value of the sensitivity index of the thermal radiation feature into several range combinations using the natural discontinuity method according to the value of the sensitivity index of the thermal radiation feature and the corresponding number of pixels, for example: the first range combination: [x1][x2, x3, ..., x m ], the m-1th range combination: [x1, x2, ..., x m-1 ][x m ]

[0089] The sum of squared deviations of the mean values ​​of the sensitivity indexes of the thermal radiation characteristics of the first range combination is calculated as follows:

[0090]

[0091] In the formula, is the sum of squared deviations of the mean of the sensitivity index of the thermal radiation feature.

[0092] The sum of square deviations of the mean values ​​of the sensitivity indexes of the thermal radiation characteristics of the m-1 range combination is calculated as follows:

[0093]

[0094] In this embodiment, the detection device divides the value of the sensitivity index of the thermal radiation feature into several range combinations, performs iterative calculations on the several range combinations, and obtains the sum of square deviations of the mean values ​​of the sensitivity index of the thermal radiation feature of each range combination as the second sum of square deviations parameter.

[0095] S402: Obtain the variance goodness of fit according to the squared deviation parameter and the variance goodness of fit algorithm, classify the sensitivity index data set of the thermal radiation feature according to the variance goodness of fit and a preset variance goodness of fit threshold, and obtain the surface type classification data of the target area.

[0096] The variance goodness-of-fit algorithm is:

[0097]

[0098] Wherein, GVF is the goodness of fit of the variance;

[0099] The detection device inputs the first square sum of deviation parameter and the second square sum of deviation parameter into the variance fit goodness algorithm, obtains the variance fit goodness of the sensitivity index of the thermal radiation feature, and classifies the sensitivity index data set of the thermal radiation feature according to the variance fit goodness and a preset variance fit goodness threshold, and obtains the surface type classification data of the target area. In an optional embodiment, the variance fit goodness threshold is set to 0.9, and when the variance fit goodness is greater than or equal to the variance fit goodness threshold, a comparison success result is obtained, and when the variance fit goodness is less than the variance fit goodness threshold, a comparison failure result is obtained, and according to the comparison success result, the sensitivity index of the thermal radiation feature whose variance fit goodness is greater than or equal to the variance fit goodness threshold is extracted from the sensitivity index data set of the thermal radiation feature, and the extracted sensitivity index of the thermal radiation feature is classified to obtain the surface type classification data of the target area.

[0100] S5: In response to a detection instruction, the detection instruction includes a remote sensing image dataset and a temperature dataset of the area to be detected, obtaining electronic map data, obtaining surface type classification data of the area to be detected based on the remote sensing image dataset and the temperature dataset of the area to be detected, obtaining surface identifiers of various areas of the electronic map data based on the surface types in the surface type classification data and the surface identifiers corresponding to the surface types, and displaying and marking the surface identifiers on the electronic map data.

[0101] The detection instruction is issued by the user and received by the detection device.

[0102] In this embodiment, the detection device obtains the detection instruction sent by the user, responds, and obtains the electronic map data. The detection device obtains the surface type classification data of the area to be detected based on the remote sensing image data set and the temperature data set of the area to be detected, obtains the identifier corresponding to the surface type according to the surface type in the surface type classification data, and returns it to the display interface of the detection device, and displays and marks the surface type and the corresponding surface identifier on the electronic map.

[0103] Please refer to Figure 6 , Figure 6 A schematic diagram of the structure of a surface type detection device based on UAV remote sensing images provided by an embodiment of the present application. The device can implement all or part of the surface type detection device based on UAV remote sensing images through software, hardware, or a combination of both. The device 6 includes:

[0104] An acquisition module 61 is used to acquire a remote sensing image data set and a temperature data set of a target area through a drone, wherein the remote sensing image data set includes a plurality of thermal infrared image data, and the temperature data set includes a temperature parameter corresponding to the thermal infrared image data;

[0105] A processing module 62, used for performing mask processing on the thermal infrared image data to obtain impermeable surface image data;

[0106] A construction module 63 is used to obtain the sensitivity index of the thermal radiation feature of each pixel of the impervious surface image data of the target area according to the impervious surface image data and the temperature data set, and to construct a sensitivity index data set of the thermal radiation feature;

[0107] A classification module 64, configured to obtain surface type classification data of the target area according to the sensitivity index data set of the thermal radiation characteristics;

[0108] The detection module 65 is used to respond to the detection instruction, the detection instruction includes the remote sensing image data set and the temperature data set of the area to be detected, obtain the electronic map data, obtain the surface type classification data of the area to be detected according to the remote sensing image data set and the temperature data set of the area to be detected, obtain the surface identification of each area of ​​the electronic map data according to the surface type in the surface type classification data and the surface identification corresponding to the surface type, and display and mark the surface identification on the electronic map data

[0109] In an embodiment of the present application, a remote sensing image data set and a temperature data set of a target area are acquired by a drone through an acquisition module, wherein the remote sensing image data set includes a plurality of thermal infrared image data, and the temperature data set includes air temperature parameters corresponding to the thermal infrared image data; a mask processing is performed on the thermal infrared image data through a processing module to acquire impermeable surface image data; a sensitivity index of thermal radiation characteristics of each pixel of the impermeable surface image data of the target area is acquired through a construction module according to the impermeable surface image data and the temperature data set, and a sensitivity index data set of thermal radiation characteristics is constructed; a classification module is used to acquire surface type classification data of the target area according to the sensitivity index data set of thermal radiation characteristics; an electronic map data is acquired through a detection module in response to a detection instruction, wherein the detection instruction includes a remote sensing image data set and a temperature data set of a to-be-detected area; surface type classification data of the to-be-detected area is acquired according to the remote sensing image data set and the temperature data set of the to-be-detected area; surface types of the to-be-detected area are acquired according to the surface types in the surface type classification data and the surface identifiers corresponding to the surface types, and surface identifiers of various areas of the electronic map data are acquired, and surface identifiers are displayed and annotated on the electronic map data. The present application processes the thermal infrared image of the target area, obtains the impermeable surface image data, analyzes the thermal radiation characteristics of the impermeable surface image data, constructs a sensitivity index data set of the thermal radiation characteristics, and classifies the surface types of the target area according to the sensitivity index data set of the thermal radiation characteristics. By combining the thermal radiation characteristics, the surface types are classified to reflect the temperature resistance performance of different surface types, accurately reflect the surface temperature of the surface type, and thus accurately reflect the thermal environment of the current area in an efficient and fast manner.

[0110] Please refer to Figure 7 , Figure 7 The computer device 7 is a schematic diagram of a structure of a computer device provided in an embodiment of the present application. The computer device 7 includes: a processor 71, a memory 72, and a computer program 73 stored in the memory 72 and executable on the processor 71; the computer device may store a plurality of instructions, which are suitable for being loaded and executed by the processor 71. Figures 1 to 5 The method steps of the embodiment shown in the figure can be found in the specific implementation process. Figures 1 to 5 The specific description of the illustrated embodiment will not be repeated here.

[0111] Among them, the processor 71 may include one or more processing cores. The processor 71 uses various interfaces and lines to connect various parts in the server, and executes various functions and processes data of the surface type detection device 6 based on drone remote sensing images by running or executing instructions, programs, code sets or instruction sets stored in the memory 72, and calling the data in the memory 72. Optionally, the processor 71 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programble Logic Array, PLA). The processor 71 can integrate one or more combinations of a central processing unit 71 (Central Processing Unit, CPU), an image processor 71 (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the touch display; and the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 71, and it can be implemented by a single chip.

[0112] Among them, the memory 72 may include a random access memory 72 (Random Access Memory, RAM), and may also include a read-only memory 72 (Read-Only Memory). Optionally, the memory 72 includes a non-transitory computer-readable storage medium. The memory 72 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 72 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch instructions, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 72 may also be optionally at least one storage device located away from the aforementioned processor 71.

[0113] The present application also provides a storage medium that can store multiple instructions, which are suitable for the processor to load and execute the above-mentioned Figures 1 to 5 The method steps of the embodiment shown in the figure can be found in the specific implementation process. Figures 1 to 5 The specific description of the illustrated embodiment will not be repeated here.

[0114] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0115] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0116] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraint algorithm of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0117] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0118] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0119] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0120] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc.

[0121] The present invention is not limited to the above-mentioned embodiments. If various changes or modifications to the present invention do not depart from the spirit and scope of the present invention, and if these changes and modifications fall within the scope of the claims and equivalent technologies of the present invention, the present invention is also intended to include these changes and modifications.

Claims

1. A method for detecting land surface types based on UAV remote sensing images, characterized in that: The following steps are involved: Acquire a remote sensing image data set and a temperature data set of a target area by using a drone, wherein the remote sensing image data set includes a plurality of thermal infrared image data, and the temperature data set includes air temperature parameters corresponding to the thermal infrared image data; Performing mask processing on the thermal infrared image data to obtain impermeable surface image data; According to the impermeable surface image data and the surface temperature algorithm, the surface temperature data of the impermeable surface image is obtained, wherein the surface temperature algorithm is: Where, T s is the surface temperature; C is the brightness temperature; C' is the equivalent atmospheric average temperature; a and b are regression coefficients; α and β are intermediate coefficients, where α = e·r, β = (1-e)[1+e(1-r)], e is the atmospheric transmittance, and r is the surface emissivity; According to the surface temperature data and the sensitivity index algorithm of thermal radiation characteristics, the sensitivity index of thermal radiation characteristics of each pixel of the impervious surface image data of the target area is obtained, and a sensitivity index data set of thermal radiation characteristics is constructed, wherein the sensitivity index algorithm of thermal radiation characteristics is: TCI=(T s -T a_c ) / (T a_max -T a_min ) Where TCI is the sensitivity index of the thermal radiation feature, T a_c is the local temperature when the thermal infrared image data is acquired in the temperature parameter, T a_max is the highest temperature of the day when the thermal infrared image data is acquired in the temperature parameter, T a_min The lowest temperature of the day when the thermal infrared image data in the temperature parameter is obtained; Acquiring the surface type classification data of the target area according to the sensitivity index data set of the thermal radiation characteristics; In response to a detection instruction, the detection instruction includes a remote sensing image data set and a temperature data set of the area to be detected, obtaining electronic map data, obtaining surface type classification data of the area to be detected based on the remote sensing image data set and the temperature data set of the area to be detected, obtaining surface identifiers of various areas of the electronic map data based on the surface types in the surface type classification data and the surface identifiers corresponding to the surface types, and displaying and marking the surface identifiers on the electronic map data.

2. The method for detecting land surface types based on UAV remote sensing images according to claim 1, characterized in that: The step of performing mask processing on the thermal infrared image data to obtain the impermeable surface image data comprises the following steps: Acquire mask data corresponding to the thermal infrared image data; According to the mask data, the area corresponding to the mask data in the thermal infrared image data of the target area is filtered to obtain the impermeable surface image data.

3. The method for detecting land surface types based on UAV remote sensing images according to claim 2, characterized in that: The mask data, wherein the mask data includes vegetation mask data, water body mask data and soil mask data, and the area corresponding to the mask data includes a vegetation area, a water body area and a soil area.

4. The method for detecting land surface types based on UAV remote sensing images according to claim 3 is characterized in that: The step of obtaining mask data corresponding to the thermal infrared image data comprises the following steps: According to the thermal infrared image data and the normalized vegetation index algorithm, a normalized vegetation index of the thermal infrared image data is obtained; according to the normalized vegetation index and a preset normalized vegetation index threshold, vegetation area image data is extracted from the thermal infrared image data; according to the vegetation area image data, vegetation mask data is obtained, wherein the normalized vegetation index algorithm is: Wherein, NDVI is the normalized vegetation index, NIR is the near infrared band, and R is the infrared band; According to the thermal infrared image data and the normalized difference water index algorithm, a normalized difference water index of the thermal infrared image data is obtained; according to the normalized difference water index and a preset normalized difference water index threshold, water body area image data is extracted from the thermal infrared image data; according to the water body area image data, water body mask data is obtained, wherein the normalized difference water index algorithm is: Wherein, MNDWI is the normalized difference water index, Green is the green band, and MIR is the mid-infrared band; According to the thermal infrared image data and the soil adjustment vegetation index algorithm, a soil adjustment vegetation index of the thermal infrared image data is obtained; according to the soil adjustment vegetation index and a preset soil adjustment vegetation index threshold, soil area image data is extracted from the thermal infrared image data; according to the soil area image data, soil mask data is obtained, wherein the soil adjustment vegetation index algorithm is: Wherein, MSAVI is the soil adjusted vegetation index, and Red is the red light band.

5. The method for detecting land surface types based on UAV remote sensing images according to claim 1, characterized in that: The step of obtaining the surface type classification data of the target area according to the sensitivity index data set of the thermal radiation characteristics further includes the steps of: Obtaining a square deviation parameter of the sensitivity index data set of the thermal radiation feature, wherein the square deviation parameter includes a first square deviation parameter and a second square deviation parameter, the first square deviation parameter is the square deviation sum of the mean of the sensitivity index of the thermal radiation feature, and the second square deviation parameter is the square deviation sum of the square of the mean of the sensitivity index of the thermal radiation feature; According to the square sum of deviation parameters and the variance goodness of fit algorithm, the variance goodness of fit is obtained, and according to the variance goodness of fit and a preset variance goodness of fit threshold, the sensitivity index data set of the thermal radiation feature is classified to obtain the surface type classification data of the target area.

6. A surface type detection device based on UAV remote sensing images, characterized in that: include: An acquisition module, used to acquire a remote sensing image data set and a temperature data set of a target area through a UAV, wherein the remote sensing image data set includes a plurality of thermal infrared image data, and the temperature data set includes air temperature parameters corresponding to the thermal infrared image data; A processing module, used for performing mask processing on the thermal infrared image data to obtain impermeable surface image data; A construction module is used to obtain the surface temperature data of the impermeable surface image according to the impermeable surface image data and the surface temperature algorithm, wherein the surface temperature algorithm is: Where, T s is the surface temperature; C is the brightness temperature; C' is the equivalent atmospheric average temperature; a and b are regression coefficients; α and β are intermediate coefficients, where α = e·r, β = (1-e)[1+e(1-r)], e is the atmospheric transmittance, and r is the surface emissivity; According to the surface temperature data and the sensitivity index algorithm of thermal radiation characteristics, the sensitivity index of thermal radiation characteristics of each pixel of the impervious surface image data of the target area is obtained, and a sensitivity index data set of thermal radiation characteristics is constructed, wherein the sensitivity index algorithm of thermal radiation characteristics is: TCI=(T s -T a_c ) / (T a_max -T a_min ) Where TCI is the sensitivity index of the thermal radiation feature, T a_c is the local temperature when the thermal infrared image data is acquired in the temperature parameter, T a_max is the highest temperature of the day when the thermal infrared image data is acquired in the temperature parameter, T a_min The lowest temperature of the day when the thermal infrared image data in the temperature parameter is obtained; A classification module, used for obtaining the surface type classification data of the target area according to the sensitivity index data set of the thermal radiation characteristics; A detection module is used to respond to a detection instruction, wherein the detection instruction includes a remote sensing image data set and a temperature data set of an area to be detected, obtain electronic map data, obtain surface type classification data of the area to be detected based on the remote sensing image data set and the temperature data set of the area to be detected, obtain surface identification of each area of ​​the electronic map data based on the surface type in the surface type classification data and the surface identification corresponding to the surface type, and display and mark the surface identification on the electronic map data.

7. A computer device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor; when the computer program is executed by the processor, the steps of the method for detecting surface types based on unmanned aerial vehicle remote sensing images as described in any one of claims 1 to 5 are implemented.

8. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the surface type detection method based on drone remote sensing images as described in any one of claims 1 to 5 are implemented.

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