Geological disaster influence range accurate measurement method based on remote sensing analysis

Abstract

The invention relates to the technical field of remote sensing analysis, and discloses a geological disaster influence range accurate measurement method based on remote sensing analysis, and the method comprises the steps: collecting a disaster influence remote sensing image, and carrying out the preprocessing; performing edge enhancement on the preprocessed disaster influence remote sensing image; performing edge optimization processing on the enhanced disaster influence remote sensing image; performing edge extraction on the optimized disaster influence remote sensing image; and performing mapping measurement on the edge contour of the disaster influence range to obtain a real disaster influence range. According to low-frequency information and high-frequency information of a disaster influence remote sensing image under different scales, extracted information is converted into an information gradient matrix representing neighborhood information changes, a gradient enhancement mode is adopted to carry out morphological processing on the information gradient matrix, noise pixels in the information gradient matrix are filtered, edge features of the pixels are enhanced, and the image quality is improved. The method achieves the extraction of the edge contour of the disaster influence range in the disaster influence remote sensing image, and carries out the measurement of the disaster influence range.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing analysis, and in particular to a method for accurately measuring the impact range of geological disasters based on remote sensing analysis. Background Art

[0002] Debris flow is a fast-flowing geological disaster consisting of a mixture of water, soil and rocks, which usually occurs in mountainous and hilly areas. The occurrence of debris flow is often closely related to natural factors such as heavy rainfall, snowmelt, earthquakes, etc. It is sudden and destructive, posing a serious threat to human life, property and the ecological environment. With the intensification of global climate change, the frequency and intensity of debris flow are gradually increasing, especially in some areas with fragile geological conditions, the risk of debris flow disasters has increased significantly. Traditional monitoring and assessment methods of debris flow geological disasters mainly rely on field investigations and manual measurements. This method is not only time-consuming and labor-intensive, but also after the disaster occurs, the field conditions are often complex, leading to difficulties in data collection. Summary of the invention

[0003] In view of this, the present invention provides a method for accurately measuring the impact range of geological disasters based on remote sensing analysis. By acquiring surface information through remote sensing technology, the affected range of debris flows can be quickly and accurately obtained over a large area, providing important support for disaster assessment and emergency response.

[0004] To achieve the above object, the present invention provides a method for accurately measuring the impact range of geological disasters based on remote sensing analysis, comprising the following steps: S1: Collect and preprocess the disaster impact remote sensing image to obtain the preprocessed disaster impact remote sensing image; S2: performing edge enhancement on the preprocessed disaster impact remote sensing image to obtain an enhanced disaster impact remote sensing image, wherein improved morphological gradient processing is an implementation method of the edge enhancement; S3: performing edge optimization processing on the enhanced disaster impact remote sensing image to reduce noise information of edge pixels to obtain an optimized disaster impact remote sensing image, wherein pixel information entropy is an implementation method of the edge optimization processing; S4: extracting the edge of the optimized disaster impact remote sensing image to obtain the edge contour of the disaster impact range; S5: Map and measure the edge contour of the disaster impact area to obtain the actual disaster impact area.

[0005] As a further improvement method of the present invention: Optionally, collecting remote sensing images of disaster impacts in step S1 includes: The remote sensing sensors on the remote sensing platform are used to collect remote sensing images of disaster impacts in the target area. The remote sensing platform is an artificial satellite, the remote sensor is a multispectral scanner, and the target area is the area after the geological disaster. The collection process of remote sensing images of disaster impacts is as follows: Select filters corresponding to the green light, near infrared, red light and short-wave infrared bands respectively, and place the selected filters into the remote sensor in turn; The infrared radiation and visible light radiation emitted from the target area enter the remote sensor and pass through filters of different bands to form filtered reflection images in different bands: ; in: represents the filtered reflection image under the u-th band, wherein the 1st to 4th bands are green light, near infrared, red light and short-wave infrared bands, respectively; in the embodiment of the present invention, the wavelengths selected for the green light, near infrared, red light and short-wave infrared bands are 510 nanometers, 820 nanometers, 675 nanometers and 2000 nanometers, respectively, which respectively characterize the imaging information of the vegetation, soil, water body and mineral rock characteristics of the target area; Represents the filtered reflection image The corresponding pixel matrix, Represents the filtered reflection image The pixel value of the pixel in the xth row and yth column, where X represents the number of pixel rows of the filtered reflection image and Y represents the number of pixel columns of the filtered reflection image; The filtered reflection images in multiple bands are used as disaster impact remote sensing images, and the disaster impact remote sensing images are preprocessed.

[0006] Optionally, the preprocessing of the disaster impact remote sensing image includes: Preprocess the remote sensing images affected by disasters, and the preprocessing process is as follows: S11: Obtain the gain coefficient and offset of the remote sensor in different bands; S12: Using the gain coefficient and the offset, the pixel value of the filtered reflection image in the corresponding band is converted into the radiation brightness; where the pixel value The radiation brightness conversion formula is: ; in: Represents pixel value The radiance of represents the gain coefficient of the remote sensor in the uth band; Indicates the offset of the remote sensor in the uth band; S13: Performing atmospheric correction on the filtered reflection image converted into the radiance to obtain an atmospherically corrected filtered reflection image; wherein the radiance The correction formula is: ; in: Represents the mean radiance of the image area with the lowest radiance in the filtered reflection image; represents the atmospheric transmittance in the u-th band; S14: Constructing pre-processed disaster impact remote sensing images ,in represents the filtered reflection image after atmospheric correction in the u-th band, Represents the filtered reflection image The radiance value of the pixel at row x and column y in .

[0007] Optionally, in step S2, edge enhancement is performed on the pre-processed disaster impact remote sensing image, including: The pre-processed disaster impact remote sensing image is edge enhanced, and the edge enhancement process is as follows: S21: splitting the pre-processed disaster impact remote sensing image into filtered reflection images in different bands; S22: Extract low-frequency information and high-frequency information at multiple scales from filtered reflection images in different bands. The formula for extracting low-frequency information at scale k is: ; in: Represents the filtered reflection image Low-frequency information at scale k; , K represents the preset maximum scale; Represents the filtered reflection image The x-th row of the radiance value sequence; represents an exponential function with a natural constant as base; Filtered reflected image The formula for extracting high-frequency information at scale k is: ; in: Represents the filtered reflection image High-frequency information at scale k; S23: Calculate the information gradient matrix of different low-frequency information and high-frequency information; The corresponding information gradient matrix for: ; ; in: Indicates low frequency information The information gradient at position c; High frequency information The corresponding information gradient matrix for: ; ; in: Represents high frequency information The information gradient at position c; S24: Perform morphological enhancement on the information gradient matrix, and use the central element of the information gradient matrix after morphological enhancement as the enhancement information; where the information gradient matrix The morphological enhancement process is: S241: Generate structure matrix ; S242: Use the structure matrix A to the information gradient matrix Perform morphological corrosion operations and morphological dilation operations in sequence to obtain the information gradient matrix after morphological enhancement. ; S243: The information gradient matrix after morphological enhancement The central element of the information gradient matrix Enhanced information ; S25: Reconstruct the enhanced information corresponding to the low-frequency information and the high-frequency information at K scales to obtain a filtered reflection image after edge enhancement, where K represents a preset maximum scale, and the filtered reflection image The reconstruction formula of the corresponding enhanced information is: ; in: Represents the enhanced information corresponding to the low-frequency information at the kth scale; Represents the enhanced information corresponding to the high-frequency information at the kth scale; Represents the filtered reflection image The reconstruction result of the x-th row of the radiance value sequence; The filtered reflected image The corresponding reconstruction result is , T represents transpose; S26: Use the filtered reflection images reconstructed in multiple bands as enhanced disaster impact remote sensing images: ; ; in: represents the enhanced disaster impact remote sensing image, express The multi-band radiance value of the pixel in the xth row and yth column, express The radiance value of the pixel in the xth row and yth column in the uth band.

[0008] Optionally, in step S3, edge optimization processing is performed on the enhanced disaster impact remote sensing image, including: Enhanced disaster impact remote sensing images Perform edge optimization processing, wherein the pixel information entropy is the implementation method of the edge optimization processing, wherein the edge optimization processing flow is: S31: Calculate the remote sensing image of disaster impact The maximum radiance value of any pixel in the remote sensing image The maximum radiance value of the pixel in row x and column y is : ; S32: Calculate the remote sensing image of the disaster impact The pixel information entropy of any pixel in the disaster-affected remote sensing image The pixel information entropy of the pixel in the xth row and yth column is: ; in: Representing disaster impact remote sensing images The pixel information entropy of the pixel in the xth row and yth column; S33: Calculate the remote sensing image of the disaster impact The radiance gradient of any pixel in the remote sensing image The radiation brightness gradient of the pixel in the xth row and yth column is : ; ; ; in: Representing disaster impact remote sensing images The maximum radiance change value of the pixel in the xth row and yth column in the horizontal direction; Representing disaster impact remote sensing images The maximum radiant brightness change value of the pixel in the xth row and yth column in the vertical direction; S34: Based on the pixel radiation brightness gradient and pixel information entropy, the maximum radiation brightness value of the pixel is edge-tuned, and the maximum radiation brightness value after edge tuning constitutes the remote sensing image of the disaster impact Edge optimization results ,in Indicates the maximum radiance value Edge tuning results.

[0009] Optionally, in step S34, edge tuning is performed on the maximum radiance value of the pixel based on the radiance gradient of the pixel and the pixel information entropy, including: The maximum radiance value The edge tuning formula is: ; in: Indicates the maximum radiance value Edge tuning results; Indicates the preset pixel information entropy threshold.

[0010] Optionally, performing edge extraction on the optimized disaster impact remote sensing image in step S4 includes: The optimized disaster impact remote sensing image F is subjected to edge extraction, wherein the edge extraction process is as follows: S41: Calculate the membership degree of any pixel in the disaster impact remote sensing image F to the edge pixel set, where the membership degree of the pixel in the xth row and yth column in the disaster impact remote sensing image F is: ; in: It represents the membership degree of the pixel in the xth row and yth column in the disaster impact remote sensing image F; represents the membership control parameter; S42: Convert the pixel membership into a probability coefficient, where the membership The corresponding probability coefficient is : ; ; in: It represents the gradient direction of the pixel in the xth row and yth column of the remote sensing image F affected by the disaster; It means that the pixel in the xth row and yth column of the disaster impact remote sensing image F is taken as the starting point and the direction is The maximum value in the straight line area of ​​; represents the membership control coefficient; S43: Mark pixels whose probability coefficients are higher than a preset threshold as edge pixels to form an edge contour of the disaster impact range.

[0011] Optionally, mapping and measuring the edge contour of the disaster impact area in step S5 includes: Mapping measurement is performed on the edge contour of the disaster impact area, and the mapping measurement process is as follows: S51: Calculate the number of edge pixels of the edge contour of the disaster impact range as the disaster perimeter Num; S52: Calculate the number of pixels within the edge contour of the disaster impact range as the disaster area H of the disaster impact range; S53: Obtain pixel values ​​and pixel coordinates of pixels within the edge contour of the disaster impact range to form a disaster pixel set: ; in: Represents the pixel information of the hth pixel within the edge contour of the disaster impact range, Represents the pixel value of the hth pixel within the edge contour of the disaster impact range, Respectively represent the coordinate values ​​of the hth pixel in the edge contour of the disaster impact range in the horizontal and vertical directions; S54: Calculate the direction of the disaster extension : ; ; in: Represents the geometric distribution moment of the disaster surrounded by the edge contour of the disaster impact range, represents the adjustment parameter; S55: The disaster perimeter, disaster area and disaster extension direction are used as characteristic parameters to characterize the actual disaster impact range.

[0012] In order to solve the above problem, the present invention provides an electronic device, the electronic device comprising: A memory storing at least one instruction; Communication interface, enabling electronic equipment to communicate; and The processor executes the instructions stored in the memory to implement the above-mentioned method for accurately measuring the impact range of geological disasters based on remote sensing analysis.

[0013] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned method for accurately measuring the impact range of geological disasters based on remote sensing analysis.

[0014] Compared with the prior art, the present invention proposes a method for accurately measuring the impact range of geological disasters based on remote sensing analysis, which has the following advantages: First, this scheme proposes an edge pixel enhancement method, which uses remote sensing analysis to collect imaging information of vegetation, soil, water bodies and mineral rock characteristics in the target area to form a disaster impact remote sensing image representing a variety of geological characteristics. According to the low-frequency information and high-frequency information of the disaster impact remote sensing image at different scales, the extracted information is converted into an information gradient matrix representing the change of neighborhood information. The information gradient matrix is ​​morphologically processed using the gradient enhancement method to filter out the noise pixels, and the pixels with large changes in radiation brightness are further enhanced to enhance the edge features of the pixels and the accuracy of disaster edge extraction.

[0015] At the same time, this scheme calculates the pixel information entropy of the pixel according to the proportion of the pixel's radiation brightness in different bands. The larger the pixel information entropy, the richer the details of the pixel in a certain band, and the greater the difference in radiation brightness with other bands. Based on the pixel's radiation brightness gradient and pixel information entropy, the maximum radiation brightness value of the pixel is edge-optimized, and the optimized radiation brightness value is converted into the membership degree and probability coefficient of the pixel belonging to the edge pixel set, so as to realize the extraction of the edge contour of the disaster impact range in the disaster impact remote sensing image, and calculate the extension direction and other distribution characteristics of the disaster according to the geometric distribution characteristics of the image surrounded by the edge contour of the disaster impact range, so as to realize the accurate measurement of the impact range of geological disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of a flow chart of a method for accurately measuring the impact range of geological disasters based on remote sensing analysis provided by one embodiment of the present invention; Figure 2 A schematic diagram of the structure of an electronic device for implementing a method for accurately measuring the impact range of geological disasters based on remote sensing analysis provided by an embodiment of the present invention.

[0017] In the figure: 1 electronic device, 10 processor, 11 memory, 12 program, 13 communication interface.

[0018] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0019] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0020] The embodiment of the present application provides a method for accurately measuring the scope of geological disaster impact based on remote sensing analysis. The execution subject of the method for accurately measuring the scope of geological disaster impact based on remote sensing analysis includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiment of the present application. In other words, the method for accurately measuring the scope of geological disaster impact based on remote sensing analysis can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0021] Embodiment 1: S1: Collect disaster impact remote sensing images and perform preprocessing to obtain preprocessed disaster impact remote sensing images.

[0022] The step S1 includes collecting remote sensing images of disaster impacts, including: The remote sensing sensors on the remote sensing platform are used to collect remote sensing images of disaster impacts in the target area. The remote sensing platform is an artificial satellite, the remote sensor is a multispectral scanner, and the target area is the area after the geological disaster. The collection process of remote sensing images of disaster impacts is as follows: Select filters corresponding to the green light, near infrared, red light and short-wave infrared bands respectively, and place the selected filters into the remote sensor in turn; The infrared radiation and visible light radiation emitted from the target area enter the remote sensor and pass through filters of different bands to form filtered reflection images in different bands: ; in: represents the filtered reflection image under the u-th band, wherein the 1st to 4th bands are green light, near infrared, red light and short-wave infrared bands, respectively; in the embodiment of the present invention, the wavelengths selected for the green light, near infrared, red light and short-wave infrared bands are 510 nanometers, 820 nanometers, 675 nanometers and 2000 nanometers, respectively, which respectively characterize the imaging information of the vegetation, soil, water body and mineral rock characteristics of the target area; Represents the filtered reflection image The corresponding pixel matrix, Represents the filtered reflection image The pixel value of the pixel in the xth row and yth column, where X represents the number of pixel rows of the filtered reflection image and Y represents the number of pixel columns of the filtered reflection image; The filtered reflection images in multiple bands are used as disaster impact remote sensing images, and the disaster impact remote sensing images are preprocessed.

[0023] The preprocessing of the remote sensing images affected by the disaster includes: Preprocess the remote sensing images affected by disasters, and the preprocessing process is as follows: S11: Obtain the gain coefficient and offset of the remote sensor in different bands; S12: Using the gain coefficient and the offset, the pixel value of the filtered reflection image in the corresponding band is converted into the radiation brightness; where the pixel value The radiation brightness conversion formula is: ; in: Represents pixel value The radiance of represents the gain coefficient of the remote sensor in the uth band; Indicates the offset of the remote sensor in the uth band; S13: Performing atmospheric correction on the filtered reflection image converted into the radiance to obtain an atmospherically corrected filtered reflection image; wherein the radiance The correction formula is: ; in: Represents the mean radiance of the image area with the lowest radiance in the filtered reflection image; represents the atmospheric transmittance in the u-th band; S14: Constructing pre-processed disaster impact remote sensing images ,in represents the filtered reflection image after atmospheric correction in the u-th band, Represents the filtered reflection image The radiance value of the pixel at row x and column y in .

[0024] S2: Perform edge enhancement on the preprocessed disaster impact remote sensing image to obtain an enhanced disaster impact remote sensing image.

[0025] In step S2, edge enhancement is performed on the pre-processed disaster impact remote sensing image, including: The pre-processed disaster impact remote sensing image is edge enhanced, and the edge enhancement process is as follows: S21: splitting the pre-processed disaster impact remote sensing image into filtered reflection images in different bands; S22: Extract low-frequency information and high-frequency information at multiple scales from filtered reflection images in different bands. The formula for extracting low-frequency information at scale k is: ; in: Represents the filtered reflection image Low-frequency information at scale k; , K represents the preset maximum scale; Represents the filtered reflection image The x-th row of the radiance value sequence; represents an exponential function with a natural constant as base; Filtered reflected image The formula for extracting high-frequency information at scale k is: ; in: Represents the filtered reflection image High-frequency information at scale k; S23: Calculate the information gradient matrix of different low-frequency information and high-frequency information; The corresponding information gradient matrix for: ; ; in: Indicates low frequency information The information gradient at position c; High frequency information The corresponding information gradient matrix for: ; ; in: Represents high frequency information The information gradient at position c; S24: Perform morphological enhancement on the information gradient matrix, and use the central element of the information gradient matrix after morphological enhancement as the enhancement information; where the information gradient matrix The morphological enhancement process is: S241: Generate structure matrix ; S242: Use the structure matrix A to the information gradient matrix Perform morphological corrosion operations and morphological dilation operations in sequence to obtain the information gradient matrix after morphological enhancement. ; S243: The information gradient matrix after morphological enhancement The central element of the information gradient matrix Enhanced information ; S25: Reconstruct the enhanced information corresponding to the low-frequency information and the high-frequency information at K scales to obtain a filtered reflection image after edge enhancement, where K represents a preset maximum scale, and the filtered reflection image The reconstruction formula of the corresponding enhanced information is: ; in: Represents the enhanced information corresponding to the low-frequency information at the kth scale; Represents the enhanced information corresponding to the high-frequency information at the kth scale; Represents the filtered reflection image The reconstruction result of the x-th row of the radiance value sequence; The filtered reflected image The corresponding reconstruction result is , T represents transpose; S26: Use the filtered reflection images reconstructed in multiple bands as enhanced disaster impact remote sensing images: ; ; in: represents the enhanced disaster impact remote sensing image, express The multi-band radiance value of the pixel in the xth row and yth column, express The radiance value of the pixel in the xth row and yth column in the uth band.

[0026] S3: Perform edge optimization processing on the enhanced disaster impact remote sensing image to reduce the noise information of edge pixels and obtain an optimized disaster impact remote sensing image.

[0027] In step S3, edge optimization processing is performed on the enhanced disaster impact remote sensing image, including: Enhanced disaster impact remote sensing images Perform edge optimization processing, wherein the pixel information entropy is the implementation method of the edge optimization processing, wherein the edge optimization processing flow is: S31: Calculate the remote sensing image of disaster impact The maximum radiance value of any pixel in the remote sensing image The maximum radiance value of the pixel in row x and column y is : ; S32: Calculate the remote sensing image of the disaster impact The pixel information entropy of any pixel in the disaster-affected remote sensing image The pixel information entropy of the pixel in the xth row and yth column is: ; in: Representing disaster impact remote sensing images The pixel information entropy of the pixel in the xth row and yth column; S33: Calculate the remote sensing image of the disaster impact The radiance gradient of any pixel in the remote sensing image The radiation brightness gradient of the pixel in the xth row and yth column is : ; ; ; in: Representing disaster impact remote sensing images The maximum radiance change value of the pixel in the xth row and yth column in the horizontal direction; Representing disaster impact remote sensing images The maximum radiant brightness change value of the pixel in the xth row and yth column in the vertical direction; S34: Based on the pixel radiation brightness gradient and pixel information entropy, the maximum radiation brightness value of the pixel is edge-tuned, and the maximum radiation brightness value after edge tuning constitutes the remote sensing image of the disaster impact Edge optimization results ,in Indicates the maximum radiance value Edge tuning results.

[0028] In the step S34, edge tuning is performed on the maximum radiance value of the pixel based on the radiance gradient of the pixel and the pixel information entropy, including: The maximum radiance value The edge tuning formula is: ; in: Indicates the maximum radiance value Edge tuning results; Indicates the preset pixel information entropy threshold.

[0029] S4: Extract the edge of the optimized disaster impact remote sensing image to obtain the edge contour of the disaster impact range.

[0030] In the step S4, edge extraction is performed on the optimized disaster impact remote sensing image, including: The optimized disaster impact remote sensing image F is subjected to edge extraction, wherein the edge extraction process is as follows: S41: Calculate the membership degree of any pixel in the disaster impact remote sensing image F to the edge pixel set, where the membership degree of the pixel in the xth row and yth column in the disaster impact remote sensing image F is: ; in: It represents the membership degree of the pixel in the xth row and yth column in the disaster impact remote sensing image F; represents the membership control parameter; S42: Convert the pixel membership into a probability coefficient, where the membership The corresponding probability coefficient is : ; ; in: It represents the gradient direction of the pixel in the xth row and yth column of the remote sensing image F affected by the disaster; It means that the pixel in the xth row and yth column of the disaster impact remote sensing image F is taken as the starting point and the direction is The maximum value in the straight line area of ​​; represents the membership control coefficient; S43: Mark pixels whose probability coefficients are higher than a preset threshold as edge pixels to form an edge contour of the disaster impact range.

[0031] S5: Map and measure the edge contour of the disaster impact area to obtain the actual disaster impact area.

[0032] The step S5 includes mapping and measuring the edge contour of the disaster impact area, including: Mapping measurement is performed on the edge contour of the disaster impact area, and the mapping measurement process is as follows: S51: Calculate the number of edge pixels of the edge contour of the disaster impact range as the disaster perimeter Num; S52: Calculate the number of pixels within the edge contour of the disaster impact range as the disaster area H of the disaster impact range; S53: Obtain pixel values ​​and pixel coordinates of pixels within the edge contour of the disaster impact range to form a disaster pixel set: ; in: Represents the pixel information of the hth pixel within the edge contour of the disaster impact range, Represents the pixel value of the hth pixel within the edge contour of the disaster impact range, Respectively represent the coordinate values ​​of the hth pixel in the edge contour of the disaster impact range in the horizontal and vertical directions; S54: Calculate the direction of the disaster extension : ; ; in: Represents the geometric distribution moment of the disaster surrounded by the edge contour of the disaster impact range; S55: The disaster perimeter, disaster area and disaster extension direction are used as characteristic parameters to characterize the actual disaster impact range.

[0033] Embodiment 2: like Figure 2 , is a schematic diagram of the structure of an electronic device for implementing a method for accurately measuring the impact range of geological disasters based on remote sensing analysis, provided by an embodiment of the present invention.

[0034] The electronic device 1 may include a processor 10 , a memory 11 , a communication interface 13 and a bus, and may also include a computer program stored in the memory 11 and executable on the processor 10 , such as a program 12 .

[0035] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 may also include both an internal storage unit of the electronic device 1 and an external storage device. The memory 11 may not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the program 12, etc., but also be used to temporarily store data that has been output or is to be output.

[0036] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (such as the program 12 for realizing accurate measurement of the scope of geological disaster impact), and calls the data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0037] The communication interface 13 may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices, and to achieve connection and communication between internal components of the electronic device.

[0038] The bus may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0039] Figure 2 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 2 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0040] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0041] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0042] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0043] The program 12 stored in the memory 11 in the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve: Collecting and preprocessing remote sensing images of disaster impacts to obtain preprocessed remote sensing images of disaster impacts; Perform edge enhancement on the preprocessed disaster impact remote sensing image to obtain an enhanced disaster impact remote sensing image; Perform edge optimization processing on the enhanced disaster impact remote sensing image to reduce the noise information of edge pixels and obtain an optimized disaster impact remote sensing image; Perform edge extraction on the optimized disaster impact remote sensing image to obtain the edge contour of the disaster impact range; The edge contour of the disaster impact area is mapped and measured to obtain the actual disaster impact area.

[0044] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figure 1 to Figure 2 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0045] It should be noted that the serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments. And the terms "including", "comprising" or any other variants thereof in this article are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "including a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0046] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0047] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for accurately measuring the impact range of geological disasters based on remote sensing analysis, characterized in that: The method comprises: S1: Collect and preprocess the disaster impact remote sensing image to obtain the preprocessed disaster impact remote sensing image; S2: performing edge enhancement on the preprocessed disaster impact remote sensing image to obtain an enhanced disaster impact remote sensing image; S3: Perform edge optimization processing on the enhanced disaster impact remote sensing image to reduce the noise information of edge pixels and obtain an optimized disaster impact remote sensing image; S4: extracting the edge of the optimized disaster impact remote sensing image to obtain the edge contour of the disaster impact range; S5: Map and measure the edge contour of the disaster impact area to obtain the actual disaster impact area.

2. The method for accurately measuring the impact range of geological disasters based on remote sensing analysis according to claim 1, characterized in that: The step S1 includes collecting remote sensing images of disaster impacts, including: The remote sensing sensors on the remote sensing platform are used to collect remote sensing images of disaster impacts in the target area. The remote sensing platform is an artificial satellite, the remote sensor is a multispectral scanner, and the target area is the area after the geological disaster. The collection process of remote sensing images of disaster impacts is as follows: Select filters corresponding to the green light, near infrared, red light and short-wave infrared bands respectively, and place the selected filters into the remote sensor in turn; The infrared radiation and visible light radiation emitted from the target area enter the remote sensor and pass through filters of different bands to form filtered reflection images in different bands: ； in: represents the filtered reflection image under the u-th band, where the 1st to 4th bands are green light, near infrared, red light and short-wave infrared bands respectively; Represents the filtered reflection image The corresponding pixel matrix, Represents the filtered reflection image The pixel value of the pixel in the xth row and yth column, where X represents the number of pixel rows of the filtered reflection image and Y represents the number of pixel columns of the filtered reflection image; The filtered reflection images in multiple bands are used as disaster impact remote sensing images, and the disaster impact remote sensing images are preprocessed.

3. The method for accurately measuring the impact range of geological disasters based on remote sensing analysis as claimed in claim 2, characterized in that: The preprocessing of the remote sensing images affected by the disaster includes: Preprocess the remote sensing images affected by disasters, and the preprocessing process is as follows: S11: Obtain the gain coefficient and offset of the remote sensor in different bands; S12: converting the pixel value of the filtered reflection image in the corresponding band into the radiance using the gain coefficient and the offset; S13: performing atmospheric correction on the filtered reflection image converted into the radiance to obtain an atmospherically corrected filtered reflection image; S14: Constructing pre-processed disaster impact remote sensing images ,in represents the filtered reflection image after atmospheric correction in the u-th band, Represents the filtered reflection image The radiance value of the pixel at row x and column y in .

4. The method for accurately measuring the impact range of geological disasters based on remote sensing analysis as claimed in claim 3, characterized in that: In step S2, edge enhancement is performed on the pre-processed disaster impact remote sensing image, including: The pre-processed disaster impact remote sensing image is edge enhanced, and the edge enhancement process is as follows: S21: splitting the pre-processed disaster impact remote sensing image into filtered reflection images in different bands; S22: extracting low-frequency information and high-frequency information at multiple scales from filtered reflection images in different bands; S23: Calculate the information gradient matrix of different low-frequency information and high-frequency information; S24: performing morphological enhancement on the information gradient matrix, and taking the central element of the information gradient matrix after morphological enhancement as the enhancement information; S25: Reconstruct the enhanced information corresponding to the low-frequency information and the high-frequency information at K scales to obtain a filtered reflection image after edge enhancement, where K represents a preset maximum scale, and the filtered reflection image The reconstruction formula of the corresponding enhanced information is: ； in: Represents the enhanced information corresponding to the low-frequency information at the kth scale; Represents the enhanced information corresponding to the high-frequency information at the kth scale; Represents the filtered reflection image The reconstruction result of the x-th row of the radiance value sequence; represents an exponential function with a natural constant as base; The filtered reflected image The corresponding reconstruction result is , T represents transpose; S26: Use the filtered reflection images reconstructed in multiple bands as enhanced disaster impact remote sensing images: ； ； in: represents the enhanced disaster impact remote sensing image, express The multi-band radiance value of the pixel in the xth row and yth column, express The radiance value of the pixel in the xth row and yth column in the uth band.

5. The method for accurately measuring the impact range of geological disasters based on remote sensing analysis as claimed in claim 4, characterized in that: In step S3, edge optimization processing is performed on the enhanced disaster impact remote sensing image, including: Enhanced disaster impact remote sensing images Perform edge optimization processing, where the edge optimization processing process is: S31: Calculate the remote sensing image of the disaster impact The maximum radiance value of any pixel in the remote sensing image The maximum radiance value of the pixel in row x and column y is ; S32: Calculate the remote sensing image of the disaster impact The pixel information entropy of any pixel in the disaster-affected remote sensing image The pixel information entropy of the pixel in the xth row and yth column is: ； in: Representing disaster impact remote sensing images The pixel information entropy of the pixel in the xth row and yth column; S33: Calculate the remote sensing image of the disaster impact The radiance gradient of any pixel in the remote sensing image The radiation brightness gradient of the pixel in the xth row and yth column is : ； ； ； in: Representing disaster impact remote sensing images The maximum radiance change value of the pixel in the xth row and yth column in the horizontal direction; Representing disaster impact remote sensing images The maximum radiant brightness change value of the pixel in the xth row and yth column in the vertical direction; S34: Based on the pixel radiation brightness gradient and pixel information entropy, the maximum radiation brightness value of the pixel is edge-optimized, and the maximum radiation brightness value after edge optimization constitutes the remote sensing image of the disaster impact Edge optimization results ,in Indicates the maximum radiance value Edge tuning results.

6. The method for accurately measuring the impact range of geological disasters based on remote sensing analysis according to claim 5, characterized in that: In step S34, edge tuning is performed on the maximum radiance value of the pixel based on the radiance gradient of the pixel and the pixel information entropy, including: The maximum radiance value The edge tuning formula is: ； in: Indicates the maximum radiance value Edge tuning results; Indicates the preset pixel information entropy threshold.

7. The method for accurately measuring the impact range of geological disasters based on remote sensing analysis according to claim 1, characterized in that: In the step S4, edge extraction is performed on the optimized disaster impact remote sensing image, including: The optimized disaster impact remote sensing image F is subjected to edge extraction, wherein the edge extraction process is as follows: S41: Calculate the membership degree of any pixel in the disaster impact remote sensing image F to the edge pixel set, where the membership degree of the pixel in the xth row and yth column in the disaster impact remote sensing image F is ; S42: Convert the pixel membership into a probability coefficient, where the membership The corresponding probability coefficient is : ； ； in: It represents the gradient direction of the pixel in the xth row and yth column of the remote sensing image F affected by the disaster; It means that the pixel in the xth row and yth column of the disaster impact remote sensing image F is taken as the starting point and the direction is The maximum value in the straight line area of ​​; represents the membership control coefficient; S43: Mark pixels whose probability coefficients are higher than a preset threshold as edge pixels to form an edge contour of the disaster impact range.

8. The method for accurately measuring the impact range of geological disasters based on remote sensing analysis according to claim 7, characterized in that: The step S5 includes mapping and measuring the edge contour of the disaster impact area, including: Mapping measurement is performed on the edge contour of the disaster impact area, and the mapping measurement process is as follows: S51: Calculate the number of edge pixels of the edge contour of the disaster impact range as the disaster perimeter Num; S52: Calculate the number of pixels within the edge contour of the disaster impact range as the disaster area H of the disaster impact range; S53: Obtain pixel values ​​and pixel coordinates of pixels within the edge contour of the disaster impact range to form a disaster pixel set: ； in: Represents the pixel information of the hth pixel within the edge contour of the disaster impact range, Represents the pixel value of the hth pixel within the edge contour of the disaster impact range, Respectively represent the coordinate values ​​of the hth pixel in the edge contour of the disaster impact range in the horizontal and vertical directions; S54: Calculate the direction of the disaster extension : ； ； in: Represents the geometric distribution moment of the disaster surrounded by the edge contour of the disaster impact range; S55: The disaster perimeter, disaster area and disaster extension direction are used as characteristic parameters to characterize the actual disaster impact range.

Images