A precise measurement method for the influence range of geological disasters based on remote sensing analysis

Abstract

The present invention relates to the technical field of remote sensing analysis, and discloses a method for accurately measuring the influence range of geological disasters based on remote sensing analysis. The method includes: collecting remote sensing images of disaster impacts and performing preprocessing; performing edge enhancement on the preprocessed remote sensing images of disaster impacts; performing edge optimization processing on the enhanced remote sensing images of disaster impacts; performing edge extraction on the optimized remote sensing images of disaster impacts; performing mapping measurement on the edge contour of the disaster influence range to obtain the true disaster influence range. According to the low-frequency information and high-frequency information of the remote sensing images of disaster impacts at different scales, the present invention converts the extracted information into an information gradient matrix representing the change of neighborhood information, and uses the gradient enhancement method to perform morphological processing on the information gradient matrix, filters the noise pixels therein, enhances the edge features of the pixels, realizes the extraction of the edge contour of the disaster influence range in the remote sensing images of disaster impacts, and measures the disaster influence range.

Description

Technical Field

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

[0002] Debris flow is a fast-flowing geological disaster composed of a mixture of water, mud, and stones, usually occurring in mountainous and hilly areas. The occurrence of debris flow is often closely related to natural factors such as heavy rainfall, snowmelt, and earthquakes, and 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 occurrence 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 methods for monitoring and assessing debris flow geological disasters mainly rely on on-site investigations and manual measurements. This method is not only time-consuming and laborious, but also after the disaster occurs, the on-site conditions are often complex, resulting in difficulties in data collection. Summary of the Invention

[0003] In view of this, the present invention provides a method for accurately measuring the influence range of geological disasters based on remote sensing analysis. By using remote sensing technology to obtain surface information, it can quickly and accurately obtain the affected range of debris flow over a large area, providing important support for disaster assessment and emergency response.

[0004] To achieve the above object, a method for accurately measuring the influence range of geological disasters based on remote sensing analysis provided by the present invention includes the following steps:

[0005] S1: Collect remote sensing images of disaster impacts and perform preprocessing to obtain preprocessed remote sensing images of disaster impacts;

[0006] S2: Perform edge enhancement on the preprocessed remote sensing images of disaster impacts to obtain enhanced remote sensing images of disaster impacts, where the improved morphological gradient processing is the implementation method of the edge enhancement;

[0007] S3: Perform edge optimization processing on the enhanced remote sensing images of disaster impacts to reduce the noise information of edge pixels and obtain optimized remote sensing images of disaster impacts, where the pixel information entropy is the implementation method of the edge optimization processing;

[0008] S4: Perform edge extraction on the optimized remote sensing images of disaster impacts to obtain the edge contour of the disaster impact range;

[0009] S5: Perform mapping measurement on the edge contour of the disaster impact range to obtain the true disaster impact range.

[0010] As a further improvement method of the present invention:

[0011] Optionally, in the step S1, collecting the remote sensing images of the disaster impact includes:

[0012] Using the remote sensor on the remote sensing platform to collect the remote sensing images of the disaster impact in the target area, where the remote sensing platform is an artificial satellite, the remote sensor is a multispectral scanner, the target area is the area after the geological disaster occurs, and the acquisition process of the remote sensing images of the disaster impact is as follows:

[0013] Select the filter plates corresponding to the green light, near-infrared, red light, and short-wave infrared bands respectively, and put the selected filter plates into the remote sensor in sequence;

[0014] The infrared light radiation and visible light radiation emitted from the target area enter the remote sensor and pass through the filter plates of different bands to form the filtered reflection images in different bands:

[0015] ;

[0016] Wherein:

[0017] represents the filtered reflection image in the u-th band, where the 1st - 4th bands are the green light, near-infrared, red light, and short-wave infrared bands in sequence; 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 in sequence, which respectively represent the imaging information of the vegetation, soil, water body, and mineral rock characteristics in the target area;

[0018] represents the filtered reflection image corresponding pixel matrix, represents the filtered reflection image the pixel value of the pixel in the x-th row and y-th column in, 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;

[0019] Take the filtered reflection images in multiple bands as the remote sensing images of the disaster impact, and preprocess the remote sensing images of the disaster impact.

[0020] Optionally, the preprocessing of the remote sensing images of the disaster impact includes:

[0021] Preprocess the remote sensing images of the disaster impact, where the preprocessing process is as follows:

[0022] S11: Obtain the gain coefficient and offset of the remote sensor in different bands;

[0023] S12: Use the gain coefficient and offset to convert the pixel value of the filtered reflection image in the corresponding band into radiance; where the radiance conversion formula of the pixel value is:

[0024] ;

[0025] Where:

[0026] represents the pixel value of the radiance;

[0027] represents the gain coefficient of the remote sensor in the u-th band;

[0028] represents the offset of the remote sensor in the u-th band;

[0029] S13: Perform atmospheric correction on the filtered reflectance image converted to radiance to obtain the filtered reflectance image after atmospheric correction; where the radiance The correction formula is:

[0030] ;

[0031] Where:

[0032] represents the average radiance of the image area with the lowest radiance in the filtered reflectance image;

[0033] represents the atmospheric transmittance in the u-th band;

[0034] S14: Construct the preprocessed remote sensing image of the disaster impact , where represents the filtered reflectance image after atmospheric correction in the u-th band, represents the filtered reflectance image the radiance value of the pixel at the x-th row and y-th column in.

[0035] Optionally, the edge enhancement of the preprocessed remote sensing image of the disaster impact in the S2 step includes:

[0036] Perform edge enhancement on the preprocessed remote sensing image of the disaster impact, where the edge enhancement processing flow is:

[0037] S21: Split the preprocessed remote sensing image of the disaster impact into filtered reflectance images in different bands;

[0038] S22: Extract the low-frequency information and high-frequency information of the filtered reflectance images in different bands at multiple scales, where the filtered reflectance image The formula for extracting the low-frequency information at scale k is:

[0039] ;

[0040] Among them:

[0041] represents the filtered reflection image of the low-frequency information at scale k; , where K represents the preset maximum scale;

[0042] represents the filtered reflection image of the sequence of radiance values of the x-th row;

[0043] represents the exponential function with the natural constant as the base;

[0044] The filtered reflection image The formula for extracting the high-frequency information at scale k is:

[0045] ;

[0046] Among them:

[0047] represents the filtered reflection image of the high-frequency information at scale k;

[0048] S23: Calculate the information gradient matrices of different low-frequency information and high-frequency information; among them, the low-frequency information corresponding information gradient matrix is:

[0049] ;

[0050] ;

[0051] Among them:

[0052] represents the information gradient of the low-frequency information at position c;

[0053] The high-frequency information corresponding information gradient matrix is:

[0054] ;

[0055] ;

[0056] Among them:

[0057] represents the information gradient of the high-frequency information at position c;

[0058] S24: Perform morphological enhancement on the information gradient matrix, and use the central element of the information gradient matrix after morphological enhancement as the enhanced information; where the information gradient matrix The morphological enhancement process is as follows:

[0059] S241: Generate a structure matrix ;

[0060] S242: Use the structure matrix A to perform morphological erosion operation and morphological dilation operation on the information gradient matrix in sequence to obtain the information gradient matrix after morphological enhancement ;

[0061] S243: Use the central element of the information gradient matrix after morphological enhancement as the enhanced information of the information gradient matrix ; ;

[0062] S25: Reconstruct the enhanced information corresponding to the low-frequency information and high-frequency information at K scales to obtain the filtered reflection image after edge enhancement, where K represents the preset maximum scale, and the filtered reflection image The reconstruction formula for the corresponding enhanced information is:

[0063] ;

[0064] Among them:

[0065] represents the enhanced information corresponding to the low-frequency information at the kth scale;

[0066] represents the enhanced information corresponding to the high-frequency information at the kth scale;

[0067] represents the reconstruction result of the radiation luminance value sequence of the xth row in the filtered reflection image ;

[0068] The reconstruction result corresponding to the filtered reflection image is , where T represents transpose;

[0069] S26: Use the reconstructed filtered reflection images in multiple bands as the enhanced remote sensing images of disaster impacts:

[0070] ;

[0071] ;

[0072] Among them:

[0073] Represents the enhanced remote sensing image of disaster impact, represents the multi-band radiance value of the pixel at the x-th row and y-th column in represents the radiance value of the pixel at the x-th row and y-th column in the u-th band in

[0074] Optionally, in the step S3, edge optimization processing is performed on the enhanced remote sensing image of disaster impact, including:

[0075] Performing edge optimization processing on the enhanced remote sensing image of disaster impact where the pixel information entropy is the implementation method of the edge optimization processing, and the edge optimization processing flow is:

[0076] S31: Calculate the maximum radiance value of any pixel in the remote sensing image of disaster impact where the maximum radiance value of the pixel at the x-th row and y-th column in the remote sensing image of disaster impact is :

[0077] ;

[0078] S32: Calculate the pixel information entropy of any pixel in the remote sensing image of disaster impact where the pixel information entropy of the pixel at the x-th row and y-th column in the remote sensing image of disaster impact is:

[0079] ;

[0080] where:

[0081] represents the pixel information entropy of the pixel at the x-th row and y-th column in the remote sensing image of disaster impact ;

[0082] S33: Calculate the radiance gradient of any pixel in the remote sensing image of disaster impact where the radiance gradient of the pixel at the x-th row and y-th column in the remote sensing image of disaster impact is :

[0083] ;

[0084] ;

[0085] ;

[0086] where:

[0087] Represents the maximum radiance change value of the pixel at the x-th row and y-th column in the horizontal direction in the remote sensing image of disaster impact ;

[0088] Represents the maximum radiance change value of the pixel at the x-th row and y-th column in the vertical direction in the remote sensing image of disaster impact ;

[0089] S34: Based on the radiance gradient of the pixel and the pixel information entropy, perform edge optimization on the maximum radiance value of the pixel, and form the edge optimization result of the remote sensing image of disaster impact with the edge-optimized maximum radiance value , where represents the edge optimization result of the maximum radiance value .

[0090] Optionally, in the step S34, based on the radiance gradient of the pixel and the pixel information entropy, performing edge optimization on the maximum radiance value of the pixel includes:

[0091] The edge optimization formula of the maximum radiance value is:

[0092] ;

[0093] where:

[0094] represents the edge optimization result of the maximum radiance value ;

[0095] represents the preset pixel information entropy threshold

[0096] Optionally, in the step S4, performing edge extraction on the optimized remote sensing image of disaster impact includes:

[0097] Performing edge extraction on the optimized remote sensing image F of disaster impact, where the edge extraction process is:

[0098] S41: Calculate the membership degree of any pixel in the remote sensing image F of disaster impact belonging to the edge pixel set, where the membership degree of the pixel at the x-th row and y-th column in the remote sensing image F of disaster impact is:

[0099] ;

[0100] where:

[0101] represents the membership degree of the pixel at the x-th row and y-th column in the remote sensing image F of disaster impact; ​

[0102] Indicates the membership control parameter;

[0103] S42: Convert the membership degree of the pixel into a probability coefficient, where the membership degree The corresponding probability coefficient is :

[0104] ;

[0105] ;

[0106] Where:

[0107] Indicates the gradient direction of the pixel at the x-th row and y-th column in the remote sensing image F of the disaster impact;

[0108] Indicates that starting from the pixel at the x-th row and y-th column in the remote sensing image F of the disaster impact, the direction is The maximum value in the straight-line region;

[0109] Indicates the membership regulation coefficient;

[0110] S43: Mark the pixels with probability coefficients higher than the preset threshold as edge pixels to form the edge contour of the disaster impact range.

[0111] Optionally, in the step S5, mapping measurement is performed on the edge contour of the disaster impact range, including:

[0112] Perform mapping measurement on the edge contour of the disaster impact range, where the mapping measurement process is:

[0113] S51: Calculate the number of edge pixels of the edge contour of the disaster impact range as the disaster perimeter Num;

[0114] 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;

[0115] S53: Obtain the pixel values and pixel coordinates of the pixels within the edge contour of the disaster impact range to form a disaster pixel set:

[0116] ;

[0117] Where:

[0118] Indicates the pixel information of the h-th pixel within the edge contour of the disaster impact range, Indicates the pixel value of the h-th pixel within the edge contour of the disaster impact range, respectively represent the coordinate values of the h-th pixel within the marginal contour of the disaster impact range in the horizontal and vertical directions;

[0119] S54: Calculate the disaster extension direction :

[0120] ;

[0121] ;

[0122] Wherein:

[0123] represents the geometric distribution moment of the disaster surrounded by the marginal contour of the disaster impact range, represents the adjustment parameter;

[0124] S55: Take the disaster perimeter, disaster area, and disaster extension direction as characteristic parameters representing the true disaster impact range.

[0125] To solve the above problems, the present invention provides an electronic device, and the electronic device includes:

[0126] A memory that stores at least one instruction;

[0127] A communication interface that enables communication of the electronic device; and

[0128] A processor that executes the instructions stored in the memory to implement the above-mentioned precise measurement method for the geological disaster impact range based on remote sensing analysis.

[0129] To solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored, and the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned precise measurement method for the geological disaster impact range based on remote sensing analysis.

[0130] Compared with the prior art, the present invention proposes a precise measurement method for the geological disaster impact range based on remote sensing analysis, and this technology has the following advantages:

[0131] First of all, this solution proposes an edge pixel enhancement method. By using remote sensing analysis to collect imaging information of the target area in terms of vegetation, soil, water body, and mineral rock characteristics, a disaster impact remote sensing image representing various geological characteristics is formed. According to the low-frequency information and high-frequency information of the disaster impact remote sensing image at different scales, and converting the extracted information into an information gradient matrix representing the change of neighborhood information, a morphological processing is performed on the information gradient matrix by using a gradient enhancement method to filter out the noise pixels therein, and further enhance the pixels with large radiation brightness changes, enhancing the edge features of the pixels and the accuracy of disaster edge extraction.

[0132] Meanwhile, according to the proportion of the radiation luminance of pixels in different bands, the pixel information entropy of the pixels is calculated. The larger the pixel information entropy is, the richer the details of the pixels in a certain band are, and the greater the difference in radiation luminance from other bands. Based on the radiation luminance gradient and pixel information entropy of the pixels, the maximum radiation luminance value of the pixels is edge-optimized, and the optimized radiation luminance value is converted into the membership degree and probability coefficient of the pixels belonging to the edge pixel set, so as to extract the edge contour of the disaster impact range in the remote sensing image of the disaster impact. According to the geometric distribution characteristics of the image surrounded by the edge contour of the disaster impact range, the extension direction and other distribution characteristics of the disaster are calculated to achieve accurate measurement of the geological disaster impact range. BRIEF DESCRIPTION OF THE DRAWINGS

[0133] Figure 1 FIG. is a schematic flow chart of a method for accurately measuring the geological disaster impact range based on remote sensing analysis provided by an embodiment of the present invention;

[0134] Figure 2 FIG. is a schematic structural diagram of an electronic device for implementing a method for accurately measuring the geological disaster impact range based on remote sensing analysis provided by an embodiment of the present invention.

[0135] In the figure: 1 is an electronic device, 10 is a processor, 11 is a memory, 12 is a program, and 13 is a communication interface.

[0136] The implementation, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0137] 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.

[0138] An embodiment of the present application provides a method for accurately measuring the geological disaster impact range based on remote sensing analysis. The execution subject of the method for accurately measuring the geological disaster impact range based on remote sensing analysis includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for accurately measuring the geological disaster impact range 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.

[0139] Embodiment 1:

[0140] S1: Collect a remote sensing image of the disaster impact and perform preprocessing to obtain a preprocessed remote sensing image of the disaster impact.

[0141] In the S1 step, remotely sensed images of disaster impacts are collected, including:

[0142] Using the remote sensor on the remote sensing platform to collect remotely sensed images of disaster impacts in the target area, where the remote sensing platform is an artificial satellite, the remote sensor is a multispectral scanner, the target area is the area after a geological disaster, and the collection process of remotely sensed images of disaster impacts is as follows:

[0143] Select the filter plates corresponding to the green light, near-infrared, red light, and short-wave infrared bands respectively, and place the selected filter plates into the remote sensor in sequence;

[0144] The infrared light radiation and visible light radiation emitted from the target area enter the remote sensor and pass through the filter plates of different bands to form filtered reflection images in different bands:

[0145] ;

[0146] Among them:

[0147] represents the filtered reflection image in the u-th band, where the 1st - 4th bands are the green light, near-infrared, red light, and short-wave infrared bands in sequence; 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 characterize the imaging information of the target area in vegetation, soil, water body, and mineral rock characteristics in sequence;

[0148] represents the filtered reflection image corresponding pixel matrix, represents the filtered reflection image the pixel value of the pixel in the x-th row and y-th column in, 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;

[0149] Take the filtered reflection images in multiple bands as remotely sensed images of disaster impacts, and preprocess the remotely sensed images of disaster impacts.

[0150] The preprocessing of the remotely sensed images of disaster impacts includes:

[0151] Preprocess the remotely sensed images of disaster impacts, where the preprocessing process is as follows:

[0152] S11: Obtain the gain coefficient and offset of the remote sensor in different bands;

[0153] S12: Use the gain coefficient and offset to convert the pixel value of the filtered reflection image in the corresponding band into radiance; where the pixel value The radiance conversion formula is:

[0154] ;

[0155] Wherein:

[0156] represents the pixel value of the radiance;

[0157] represents the gain coefficient of the remote sensor in the u-th band;

[0158] represents the offset of the remote sensor in the u-th band;

[0159] S13: Perform atmospheric correction on the filtered reflection image converted to radiance to obtain the filtered reflection image after atmospheric correction; wherein the radiance The correction formula is:

[0160] ;

[0161] Wherein:

[0162] represents the average radiance of the image area with the lowest radiance in the filtered reflection image;

[0163] represents the atmospheric transmittance in the u-th band;

[0164] S14: Construct the preprocessed remote sensing image of the disaster impact , wherein 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 the x-th row and y-th column in.

[0165] S2: Perform edge enhancement on the preprocessed remote sensing image of the disaster impact to obtain the enhanced remote sensing image of the disaster impact.

[0166] In the step S2, performing edge enhancement on the preprocessed remote sensing image of the disaster impact includes:

[0167] Performing edge enhancement on the preprocessed remote sensing image of the disaster impact, wherein the edge enhancement processing flow is:

[0168] S21: Split the preprocessed remote sensing image of the disaster impact into filtered reflection images in different bands;

[0169] S22: Extract low-frequency information and high-frequency information at multiple scales for the filtered reflection images in different bands, wherein the filtered reflection image The formula for extracting low-frequency information at scale k is:

[0170] ;

[0171] Wherein:

[0172] represents the filtered reflection image the low-frequency information at scale k; , K represents the preset maximum scale;

[0173] represents the filtered reflection image the sequence of radiance values of the x-th row;

[0174] represents the exponential function with the natural constant as the base;

[0175] the filtered reflection image The formula for extracting the high-frequency information at scale k of the filtered reflection image is:

[0176] ;

[0177] Wherein:

[0178] represents the filtered reflection image the high-frequency information at scale k;

[0179] S23: Calculate the information gradient matrices of different low-frequency information and high-frequency information; wherein the low-frequency information the corresponding information gradient matrix is:

[0180] ;

[0181] ;

[0182] Wherein:

[0183] represents the information gradient of the low-frequency information at position c;

[0184] the high-frequency information the corresponding information gradient matrix is:

[0185] ;

[0186] ;

[0187] Wherein:

[0188] represents the high-frequency information The information gradient at position c;

[0189] S24: Perform morphological enhancement on the information gradient matrix, and use the central element of the information gradient matrix after morphological enhancement as the enhanced information; where the information gradient matrix The morphological enhancement process is as follows:

[0190] S241: Generate the structure matrix ;

[0191] S242: Use the structure matrix A to perform morphological erosion operation and morphological dilation operation on the information gradient matrix in sequence to obtain the information gradient matrix after morphological enhancement ;

[0192] S243: Use the central element of the information gradient matrix after morphological enhancement as the enhanced information of the information gradient matrix ;

[0193] S25: Reconstruct the enhanced information corresponding to the low-frequency information and high-frequency information at K scales to obtain the filtered reflection image after edge enhancement, where K represents the preset maximum scale, and the filtered reflection image The reconstruction formula for the corresponding enhanced information is:

[0194] ;

[0195] Where:

[0196] represents the enhanced information corresponding to the low-frequency information at the kth scale;

[0197] represents the enhanced information corresponding to the high-frequency information at the kth scale;

[0198] represents the reconstruction result of the sequence of radiance values in the xth row of the filtered reflection image ;

[0199] The corresponding reconstruction result of the filtered reflection image is , where T represents transpose;

[0200] S26: Use the reconstructed filtered reflection images in multiple bands as the enhanced remote sensing images of disaster impacts:

[0201] ;

[0202] ;

[0203] Wherein:

[0204] represents the enhanced remote sensing image of disaster impact, represents the multi - band radiance value of the pixel at the x - th row and y - th column in represents the radiance value of the pixel at the x - th row and y - th column in the u - th band in

[0205] S3: Perform edge optimization processing on the enhanced remote sensing image of disaster impact to reduce the noise information of edge pixels and obtain the optimized remote sensing image of disaster impact.

[0206] The edge optimization processing of the enhanced remote sensing image of disaster impact in the S3 step includes:

[0207] Perform edge optimization processing on the enhanced remote sensing image of disaster impact where the pixel information entropy is the implementation method of the edge optimization processing, and the edge optimization processing flow is:

[0208] S31: Calculate the maximum radiance value of any pixel in the remote sensing image of disaster impact where the maximum radiance value of the pixel at the x - th row and y - th column in the remote sensing image of disaster impact is :

[0209] ;

[0210] S32: Calculate the pixel information entropy of any pixel in the remote sensing image of disaster impact where the pixel information entropy of the pixel at the x - th row and y - th column in the remote sensing image of disaster impact is:

[0211] ;

[0212] Wherein:

[0213] represents the pixel information entropy of the pixel at the x - th row and y - th column in the remote sensing image of disaster impact ;

[0214] S33: Calculate the radiance gradient of any pixel in the remote sensing image of disaster impact where the radiance gradient of the pixel at the x - th row and y - th column in the remote sensing image of disaster impact is :

[0215] ;

[0216] ;

[0217] ;

[0218] Wherein:

[0219] represents the maximum radiance change value of the pixel at the x-th row and y-th column in the horizontal direction in the remote sensing image affected by the disaster; in the remote sensing image affected by the disaster;

[0220] represents the maximum radiance change value of the pixel at the x-th row and y-th column in the vertical direction in the remote sensing image affected by the disaster; in the remote sensing image affected by the disaster;

[0221] S34: Based on the radiance gradient of the pixel and the pixel information entropy, perform edge optimization on the maximum radiance value of the pixel, and form the edge optimization result of the remote sensing image affected by the disaster with the edge-optimized maximum radiance value where represents the edge optimization result of the maximum radiance value represents the maximum radiance value of the edge optimization result.

[0222] In the step S34, based on the radiance gradient of the pixel and the pixel information entropy, perform edge optimization on the maximum radiance value of the pixel, including:

[0223] The maximum radiance value The edge optimization formula is:

[0224] ;

[0225] Wherein:

[0226] represents the edge optimization result of the maximum radiance value ;

[0227] represents a preset pixel information entropy threshold.

[0228] S4: Perform edge extraction on the optimized remote sensing image affected by the disaster to obtain the edge contour of the disaster-affected area.

[0229] In the step S4, performing edge extraction on the optimized remote sensing image affected by the disaster includes:

[0230] Perform edge extraction on the optimized remote sensing image F affected by the disaster, where the edge extraction process is:

[0231] S41: Calculate the membership degree of any pixel in the remote sensing image F of the disaster impact belonging to the set of edge pixels. The membership degree of the pixel in the x-th row and y-th column of the remote sensing image F of the disaster impact is as follows:

[0232] ;

[0233] Among them:

[0234] represents the membership degree of the pixel in the x-th row and y-th column of the remote sensing image F of the disaster impact;

[0235] represents the membership degree control parameter;

[0236] S42: Convert the membership degree of the pixel into a probability coefficient. The probability coefficient corresponding to the membership degree is :

[0237] ;

[0238] ;

[0239] Among them:

[0240] represents the gradient direction of the pixel in the x-th row and y-th column of the remote sensing image F of the disaster impact;

[0241] represents the maximum value in the straight-line area starting from the pixel in the x-th row and y-th column of the remote sensing image F of the disaster impact and with the direction of ;

[0242] represents the membership degree regulation coefficient;

[0243] S43: Mark the pixels with probability coefficients higher than the preset threshold as edge pixels to form the edge contour of the disaster impact range.

[0244] S5: Perform mapping measurement on the edge contour of the disaster impact range to obtain the true disaster impact range.

[0245] In the step S5, the mapping measurement of the edge contour of the disaster impact range includes:

[0246] Perform mapping measurement on the edge contour of the disaster impact range. The mapping measurement process is as follows:

[0247] S51: Calculate the number of edge pixels of the edge contour of the disaster impact range as the disaster perimeter Num;

[0248] 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.

[0249] S53: Obtain the pixel values and pixel coordinates of the pixels within the edge contour of the disaster impact range to form a disaster pixel set:

[0250] ;

[0251] Where:

[0252] represents the pixel information of the h-th pixel within the edge contour of the disaster impact range, represents the pixel value of the h-th pixel within the edge contour of the disaster impact range, respectively represent the coordinate values of the h-th pixel within the edge contour of the disaster impact range in the horizontal and vertical directions;

[0253] S54: Calculate the disaster extension direction :

[0254] ;

[0255] ;

[0256] Where:

[0257] represents the geometric distribution moment of the disaster surrounded by the edge contour of the disaster impact range;

[0258] S55: Use the disaster perimeter, disaster area, and disaster extension direction as characteristic parameters to characterize the true disaster impact range.

[0259] Example 2:

[0260] As Figure 2 shown, it is a schematic structural diagram of an electronic device for implementing the method for accurately measuring the geological disaster impact range based on remote sensing analysis provided by an embodiment of the present invention.

[0261] The electronic device 1 may include a processor 10, a memory 11, a communication interface 13, and a bus, and may further include a computer program stored in the memory 11 and executable on the processor 10, such as program 12.

[0262] Among them, the memory 11 at least includes one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), magnetic memory, magnetic disk, optical disc, etc. The memory 11 can be an internal storage unit of the electronic device 1 in some embodiments, such as the mobile hard disk of the electronic device 1. The memory 11 can also be an external storage device of the electronic device 1 in some other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the electronic device 1. Further, the memory 11 can also include both the internal storage unit and the external storage device of the electronic device 1. The memory 11 can be used not only to store application software installed in the electronic device 1 and various types of data, such as the code of the program 12, but also to temporarily store data that has been output or will be output.

[0263] The processor 10 can be composed of integrated circuits in some embodiments. For example, it can be composed of a single packaged integrated circuit, or can be composed of multiple integrated circuits with the same or different functions packaged, including a combination of one or more Central Processing Units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, connecting various components of the entire electronic device through various interfaces and lines, and by running or executing programs or modules (such as the program 12 for accurately measuring the influence range of geological disasters) stored in the memory 11, and calling the data stored in the memory 11, to execute various functions of the electronic device 1 and process data.

[0264] The communication interface 13 can include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), and is usually used to establish a communication connection between the electronic device 1 and other electronic devices, and to achieve connection communication between internal components of the electronic device.

[0265] The bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. The bus is set to achieve connection communication between the memory 11 and at least one processor 10, etc.

[0266] Figure 2 Only an electronic device with components is shown. Those skilled in the art can understand that Figure 2 the shown structure does not constitute a limitation on the electronic device 1, and it may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0267] For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component. Preferably, the power source can be logically connected to the at least one processor 10 through a power management device, so as to implement functions such as charge management, discharge management, and power consumption management through the power management device. The power source may also include any components such as one or more DC or AC power sources, a recharge device, a power failure detection circuit, a power converter or inverter, a power status indicator, etc. The electronic device 1 may also include various sensors, a Bluetooth module, a Wi-Fi module, etc., which will not be elaborated here.

[0268] Optionally, the electronic device 1 may further include a user interface. The user interface may be a display, an input unit (such as a keyboard), and optionally, the user interface may also be 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 liquid crystal display, and an OLED (Organic Light-Emitting Diode) toucher, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display the information processed in the electronic device 1 and to display a visual user interface.

[0269] It should be understood that the above embodiments are only for illustration purposes and are not limited by this structure in the scope of the patent application.

[0270] 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 implement:

[0271] Collect disaster impact remote sensing images and perform preprocessing to obtain preprocessed disaster impact remote sensing images;

[0272] Perform edge enhancement on the preprocessed disaster impact remote sensing images to obtain enhanced disaster impact remote sensing images;

[0273] Perform edge optimization processing on the enhanced disaster impact remote sensing images to reduce the noise information of edge pixels and obtain optimized disaster impact remote sensing images;

[0274] Perform edge extraction on the optimized disaster impact remote sensing images to obtain the edge contour of the disaster impact range;

[0275] Map and measure the edge contour of the disaster impact range to obtain the true disaster impact range.

[0276] Specifically, for the specific implementation method of the above instructions by the processor 10, reference can be made to Figures 1 to 2 the description of the relevant steps in the corresponding embodiment, which will not be elaborated here.

[0277] 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 term "including", "comprising" or any other variant thereof in this article is intended to cover a non-exclusive inclusion, so that a process, device, article or method including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, device, article or method. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, device, article or method including the element.

[0278] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented 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, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0279] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally 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 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; 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; In step S2, edge enhancement is performed on the pre-processed disaster impact remote sensing image, wherein the edge enhancement processing flow 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: reconstructing the enhanced information corresponding to the low-frequency information and the high-frequency information at K scales to obtain an edge-enhanced filtered reflection image, where K represents a preset maximum scale; S26: Using the filtered reflection images reconstructed in multiple bands as enhanced remote sensing images of disaster impacts.

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 according to claim 1, characterized in that: The filtered reflected 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: The enhanced disaster impact remote sensing image: ； ； 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 disaster-affected remote sensing image F; 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.

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