Geological disaster analysis method and system based on remote sensing data fusion

Abstract

The invention belongs to the technical field of geological disaster analysis, and discloses a geological disaster analysis method and system based on remote sensing data fusion. The method comprises the following steps: acquiring influence factor data information in disaster remote sensing data, acquiring a geological disaster comprehensive analysis model by utilizing a multi-influence factor fusion method based on the acquired influence factor data information in the disaster remote sensing data, and acquiring a geological disaster comprehensive analysis image; based on the geological disaster comprehensive analysis image, obtaining regional comprehensive characteristics influenced by disasters, and visually displaying the regional comprehensive characteristics; the region comprehensive features comprise the surface landform change features of the region influenced by the disaster. According to the method, geological disasters are analyzed based on remote sensing data multi-influence factor fusion, accurate analysis information is obtained, and high-precision data information is further provided for monitoring or prediction of the geological disasters.

Description

Technical Field

[0001] The invention belongs to the technical field of geological disaster analysis, and discloses a geological disaster analysis method and system based on remote sensing data fusion. Background Art

[0002] Remote sensing refers to a comprehensive technology that, based on the theory of electromagnetic radiation, uses various sensing instruments to collect, process and finally image the electromagnetic wave information data radiated and reflected by distant targets without direct contact with the target objects, so as to detect and identify various ground scenes. After obtaining remote sensing data, the objects displayed in the remote sensing data can be visually interpreted based on manual or machine recognition to obtain data such as the height and shape of the objects. Therefore, remote sensing technology can be used to detect the topography of the area. In this context, when a disaster occurs, remote sensing technology can be used to survey the specific conditions in the disaster area, so as to quickly determine information such as the disaster occurrence point and disaster range, so as to formulate a more reasonable disaster relief plan and reduce the risk of the disaster relief process.

[0003] To solve the above problems, the invention patent: Geological hazard analysis method and system based on remote sensing data fusion (publication number CN116030354A, publication date 20230428) is published:

[0004] Step S1: Acquire a historical remote sensing data set and a disaster remote sensing data set, wherein the historical remote sensing data set includes remote sensing data measured at the same time point before a disaster occurs, and the disaster remote sensing data set includes remote sensing data measured at the same time point after a disaster occurs, fuse all remote sensing data in the historical remote sensing data set to generate a first image, and fuse all data in the disaster remote sensing data set to generate a second image;

[0005] Step S2: dividing the first image and the second image into a plurality of identical grid areas, performing visual interpretation on each grid area in the first image and the second image, respectively, and acquiring first object information data and second object information data in the first image and the second image, wherein the first object information data and the second object information data both include object names, object categories, and coordinates of the area where the objects are located;

[0006] Step S3: generating shooting numbers based on the shooting time of the first image and the second image, establishing a first table and a second table, wherein the first table and the second table respectively include the shooting numbers of the first image and the second image, and filling the first ground feature information data and the second ground feature information data into the first table and the second table respectively;

[0007] Step S4: comparing the first ground feature information data and the second ground feature information data in the first table and the second table, so as to determine the coordinates of the area affected by the disaster in the first image based on the comparison result;

[0008] Furthermore, geological disasters refer to geological effects that are caused by natural or human factors and cause damage and loss to human life, property and the environment, such as collapse, landslides, mudslides, ground fissures, soil erosion, land desertification and swamping, soil salinization, as well as earthquakes, volcanoes, geothermal damage, etc. Geological disaster prevention and control refers to changing the process of these geological disasters through effective geological engineering means to achieve the purpose of reducing or preventing disasters and minimizing the loss of life and property. Geological disasters occur under certain dynamic inductions, some of which are natural and some are man-made. Based on this, geological disasters can also be roughly divided into two categories according to their dynamic causes: natural geological disasters and man-made geological disasters. The location, scale and frequency of natural geological disasters are controlled by natural geological conditions and are not affected by the development of human history. Man-made geological disasters are restricted by human engineering development activities and often increase with social and economic development.

[0009] At present, when analyzing geological disaster information, most of the time, the disaster analysis is carried out by transmitting the geological disaster information to the computers of the corresponding disaster prediction stations. However, this analysis method is relatively limited. It cannot achieve a comprehensive analysis of geological disasters by using geological Internet big data, cannot achieve the purpose of accurately grading and analyzing the disaster level, and cannot achieve a rapid assessment of the risks of geological disasters, which brings great inconvenience to people's monitoring of geological disasters.

[0010] To solve the above problems, the invention patent, a geological disaster analysis method based on the Internet (publication number CN109034588A, publication date 20181218), discloses the following contents: S1. First, the geological disaster monitoring module is used to monitor the geological disaster situation and transmit the monitored disaster situation to the disaster management module. Then the disaster management module can transmit the monitored geological disaster situation data to the geographic Internet big data database for storage. S2. Then the disaster management module can control the disaster severity classification unit and the disaster change classification unit in the geological disaster classification system to extract the geographic information data stored in the geographic Internet big data database for disaster classification, which involves the field of geological disaster analysis technology. This Internet-based geological disaster analysis method solves the problem that the existing analysis method is relatively limited, realizes a comprehensive analysis of the geological disaster situation by using the geological Internet big data, achieves the purpose of accurately classifying and analyzing the disaster level of the disaster situation, and realizes a rapid assessment of the risk of the geological disaster situation.

[0011] Through the above analysis, the problems and defects of the existing technology are as follows: most of the existing technologies are based on monitoring one influencing factor in geological disasters, but multiple influencing factors (collapse, landslide, mudslide, ground fissures, soil erosion, land desertification and swamping, soil salinization, earthquakes, volcanoes, geothermal damage, etc.) influence each other and are constantly changing, so a comprehensive judgment is needed, but the existing technology lacks a method for analyzing geological disasters based on the fusion of multiple influencing factors of remote sensing data. Summary of the invention

[0012] In order to overcome the problems existing in the related art, the disclosed embodiment of the present invention provides a geological disaster analysis method and system based on remote sensing data fusion.

[0013] The technical solution is as follows: A geological disaster analysis method based on remote sensing data fusion, including:

[0014] S1: Obtaining the influencing factor data information in the disaster remote sensing data, which includes: collapse, landslide, mud-rock flow, ground fissure, soil erosion, land desertification, swamping, soil salinization, as well as earthquake, volcano, geothermal damage image information;

[0015] S2: Based on the acquired influencing factor data information, a comprehensive analysis model of geological hazards is obtained using a multi-influencing factor fusion method, and a comprehensive analysis image of geological hazards is obtained;

[0016] S3, based on the comprehensive analysis image of geological disasters, obtain the comprehensive characteristics of the area affected by the disaster and visualize them; the comprehensive characteristics of the area include the surface landform change characteristics of the area affected by the disaster.

[0017] In step S2, a comprehensive analysis model of geological disasters is obtained by using a multi-influencing factor fusion method, and a comprehensive analysis image of geological disasters is obtained, including:

[0018] Step 1: Input the image training sample A of the influencing factors in the disaster remote sensing data and the image testing sample B of the influencing factors in the disaster remote sensing data, which are used for the training of the geological disaster comprehensive analysis model and the evaluation of the geological disaster comprehensive analysis model respectively;

[0019] Step 2: Perform a normality test on the initial pixel label y of the influencing factor image training sample A in the disaster remote sensing data;

[0020] Step 3: According to the number of message trees N of each influencing factor in the current geological disaster zone disaster remote sensing data zone , using mark as the fusion pixel marker, establish the message tree of each influencing factor in each disaster remote sensing data Integrate the loss function deviation of the current geological disaster comprehensive analysis model;

[0021] Step 4: After the fusion of the current geological disaster comprehensive analysis model is completed, the fusion deviation error in the current geological disaster section is calculated, error = real-a, real is the pixel label of the data to be fused, a is the predicted value of the current geological disaster section for the training sample A, and the expression is:

[0022]

[0023] In the formula, is the learning rate, ∑ n_estimators is the cumulative prediction value of the current geological disaster section for training sample A, is the information tree of each influencing factor in the i-th disaster remote sensing data in the current geological disaster area The predicted value on the training set, and update the data value to be fused real = error;

[0024] Step 5: Perform a normality test on the fusion deviation error of the influencing factor image training sample A in the disaster remote sensing data;

[0025] Step 6: When the loss function of the evaluation function on the influencing factor image test sample B in the disaster remote sensing data no longer decreases within the specified number of early evolution stop stages, the training of the geological disaster comprehensive analysis model stops, and the optimal number of iteration stages N of the geological disaster comprehensive analysis model is obtained. The training of the geological disaster comprehensive analysis model is completed, and the process jumps to step 7. Otherwise, the loss function on the influencing factor image test sample B in the disaster remote sensing data continues to decrease, and the process jumps to step 4 to continue the stage iteration.

[0026] Step 7: Predict the test set of each influencing factor in the disaster remote sensing data. According to the optimal number of iteration stages N of the comprehensive analysis model of geological disasters, the prediction result D of the test set of each influencing factor in the disaster remote sensing data is obtained. The expression is:

[0027]

[0028] In the formula, The image evolution of each influencing factor in the pre-disaster remote sensing data of the pixel labeling in stage j is the image evolution of each influencing factor in the anti-disaster remote sensing data, i is the influencing factor, is the message tree of each influencing factor in the disaster remote sensing data of stage j prediction results.

[0029] In step 2, a normality test is performed on the initial pixel label y of the influencing factor image training sample A in the disaster remote sensing data, including:

[0030] (1) According to the pixel labels of the image training samples of each influencing factor in the disaster remote sensing data, the data distribution discrete bias rate P is calculated, and the expression is:

[0031]

[0032] Where real is the pixel label of the data to be fused, that is, real = y; is the mean value of sample pixel labels, ρ is the sample capacity, and std is the standard deviation of sample pixel labels;

[0033] (2) According to the pixel labels of the image training samples of each influencing factor in the disaster remote sensing data, the data distribution fusion suitable state proportion G is calculated, and the expression is:

[0034]

[0035] (3) Construct the image fusion statistics UT of each influencing factor in the disaster remote sensing data, expressed as:

[0036]

[0037] Test the significance of the UT statistic and examine the degree of bias to determine whether to change the data; if the UT statistic is greater than the threshold, proceed to step (4), otherwise proceed to step (5);

[0038] (4) The data is obviously biased. Determine whether the preset upper limit of the number of data changes is reached. Set the upper limit of the number of changes to max1≤0. If so, jump to step (5); otherwise, update the value of max1 to make max1=max1-1, perform boxcos changes on the data, and traverse the b values ​​through the following formula. Select the b that maximizes the likelihood function F of the data pixel mark after the image evolution of each influencing factor in the disaster remote sensing data to generate a change expression:

[0039]

[0040] In the formula, real b is the transformation amount of the disaster remote sensing data in the geological disaster area, ln() is the logarithm of the transformation amount, b is the transformation value that conforms to the normal distribution, and the value range is 0.00-1.00;

[0041] Get the new data pixel mark and use it as the first stage fusion pixel mark; set the current geological disaster section judgment variable jud[1] = real;

[0042] (5) If the data bias is not obvious, or the number of image evolutions of each influencing factor in the data pixel mark disaster remote sensing data reaches the target upper limit, the original data pixel mark y is taken as the first stage fusion pixel mark, and the current geological disaster section judgment variable jud[1] = False is set.

[0043] In step 3, the loss function deviation of the current geological disaster comprehensive analysis model is integrated, including:

[0044] Step 1, collect information about each influencing factor in the disaster remote sensing data to the fusion center through the communication network;

[0045] Step 2, based on the physical process and current geological hazards, the loss function deviation matrix is ​​comprehensively analyzed to develop an insertion method for artificial influence factors, so that the fusion of the virtual geological hazard interference object and the current geological hazard comprehensive analysis loss function deviation become worse;

[0046] Step 3, the fusion center decodes the received signal, and the probability of successful decoding is related to the impact value of the impact factor;

[0047] Step 4: derive the criteria for selection probability and successful decoding probability and obtain the impact factor insertion strategy.

[0048] In step 2, the method for inserting the artificial influence factor includes:

[0049] For objects without geological hazards, P is calculated based on the parameter information of the dynamic model and observation equation. i (t), construct a random matrix according to the identification process of geological disaster remote sensing data, calculate according to the local information of each influencing factor message in each geological disaster remote sensing data and mathematical operation, and obtain the variance of the artificial influencing factor;

[0050] It is difficult to obtain an accurate matrix for virtual geological disaster interference objects. Due to the existence of random matrices generated in real time, virtual geological disaster interference objects cannot obtain accurate geological disaster information.

[0051] Furthermore, the variance of artificial influence factors is obtained, including:

[0052] The components selected at time t Each influencing factor message i in the disaster remote sensing data is sent to the fusion center, and the artificial influencing factor a is inserted before sending. i (t)Add to A signal is sent over a communication channel.

[0053] In step 2, the loss function deviation matrix is ​​comprehensively analyzed to develop an insertion method for artificial influence factors, including:

[0054] 2.1, for any initial condition P(t), the fusion mechanism achieves perfect expected fusion when both of the following conditions are met:

[0055]

[0056] Where P e(t) is the deviation of the current geological disaster comprehensive analysis loss function calculated by the virtual geological disaster interference object, H i (t) is a diagonal element, θ i (t) is the matrix null space;

[0057] 2.2, select the artificial impact factor influence value a i (t), using the matrix of the orthogonal basis, we get the matrix null space θ i (t) an orthonormal basis;

[0058] 2.3, Artificial influence factor a i (t) is given by:

[0059] a i (t) = θ i (t)ζ i (t)

[0060] In the formula, ζ i (t) is the component that is the impact factor.

[0061] In step 6, the calculation of the loss function on the impact factor image test sample B in the disaster remote sensing data specifically includes:

[0062] Calculate the prediction value of the comprehensive analysis model of geological disasters in the zone of the current geological disaster section on the image test sample B of the influencing factors in the disaster remote sensing data, and the image evolution of each influencing factor in the pre-disaster remote sensing data of the stage j pixel mark in the anti-disaster remote sensing data The expression is:

[0063]

[0064] Where, jud[j] is the judgment variable of the current geological disaster section j; sigmod -1 ( ) is the image evolution of each influencing factor in the sigmod disaster remote sensing data and the image evolution of each influencing factor in the anti-disaster remote sensing data, boxcos -1 The image evolution of each influencing factor in the BOXCOS disaster remote sensing data is the image evolution of each influencing factor in the anti-disaster remote sensing data; the expression is:

[0065]

[0066]

[0067] Furthermore, the loss function value of the current geological disaster section is calculated, and the expression is:

[0068] SU=F(a,y B )

[0069] In the formula, y B is the initial pixel label of the test set B for each influencing factor in the disaster remote sensing data, F() is the loss function used by the system, which is usually a square loss function for regression problems. In this case, SU=(ay B ) 2 .

[0070] Another object of the present invention is to provide a geological disaster analysis system based on remote sensing data fusion, which is implemented by the geological disaster analysis method based on remote sensing data fusion, and the system includes:

[0071] The influencing factor data information acquisition module is used to obtain the influencing factor data information in the disaster remote sensing data, and the influencing factor data information includes collapse, landslide, mud-rock flow, ground fissure, soil erosion, land desertification and swamping, soil salinization, as well as earthquake, volcano, geothermal damage image information;

[0072] The multi-influencing factor fusion module is used to obtain a comprehensive analysis model of geological disasters and a comprehensive analysis image of geological disasters based on the influencing factor data information in the acquired disaster remote sensing data by using the multi-influencing factor fusion method;

[0073] The module for acquiring comprehensive characteristics of the area affected by the disaster is used to acquire the comprehensive characteristics of the area affected by the disaster based on the comprehensive analysis image of the geological disaster, and to visualize the characteristics; the comprehensive characteristics of the area include the characteristics of the surface landform changes in the area affected by the disaster.

[0074] In combination with all the above-mentioned technical solutions, the beneficial effects of the present invention are as follows: the present invention obtains accurate analysis information by analyzing geological disasters based on the fusion of multiple influencing factors of remote sensing data, further provides high-precision data information for the monitoring or prediction of geological disasters, and provides a theoretical basis for geological disaster monitoring and the corresponding guidance plans adopted in the later stage. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] The accompanying drawings herein are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description, serve to explain the principles of the present disclosure;

[0076] Figure 1 It is a flow chart of a geological disaster analysis method based on remote sensing data fusion provided by an embodiment of the present invention;

[0077] Figure 2 It is a schematic diagram of geological disaster analysis based on remote sensing data fusion provided by an embodiment of the present invention;

[0078] In the figure: 1. Influencing factor data information acquisition module; 2. Multiple influencing factor fusion module; 3. Comprehensive characteristics acquisition module of disaster-affected areas. DETAILED DESCRIPTION

[0079] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific implementation disclosed below.

[0080] Embodiment 1, as Figure 1 As shown in the figure, the geological disaster analysis methods based on remote sensing data fusion include:

[0081] S1: Obtaining the influencing factor data information in the disaster remote sensing data, which includes: collapse, landslide, mud-rock flow, ground fissure, soil erosion, land desertification, swamping, soil salinization, as well as earthquake, volcano, geothermal damage image information;

[0082] S2: Based on the acquired influencing factor data information, a comprehensive analysis model of geological hazards is obtained using a multi-influencing factor fusion method, and a comprehensive analysis image of geological hazards is obtained;

[0083] S3, based on the comprehensive analysis image of geological disasters, obtain the comprehensive characteristics of the area affected by the disaster and visualize them; the comprehensive characteristics of the area include the surface landform change characteristics of the area affected by the disaster.

[0084] In step S2, a comprehensive analysis model of geological disasters is obtained by using a multi-influencing factor fusion method, and a comprehensive analysis image of geological disasters is obtained, including:

[0085] Step 1: Input the image training sample A of the influencing factors in the disaster remote sensing data and the image testing sample B of the influencing factors in the disaster remote sensing data, which are used for the training of the geological disaster comprehensive analysis model and the evaluation of the geological disaster comprehensive analysis model respectively;

[0086] Step 2: Perform a normality test on the initial pixel label y of the influencing factor image training sample A in the disaster remote sensing data;

[0087] Step 3: According to the number of message trees N of each influencing factor in the current geological disaster zone disaster remote sensing data zone , using mark as the fusion pixel marker, establish the message tree of each influencing factor in each disaster remote sensing data Integrate the loss function deviation of the current geological disaster comprehensive analysis model; specifically include:

[0088] Step 1, collect information about each influencing factor in the disaster remote sensing data to the fusion center through the communication network;

[0089] Step 2: Based on the physical process and current geological hazards, the loss function deviation matrix is ​​comprehensively analyzed to develop an insertion method for artificial influence factors, so that the integration of virtual geological hazard interference objects and the current geological hazard comprehensive analysis loss function deviation become worse; for geological hazard non-interference objects, P is calculated based on the parameter information of the dynamic model and observation equation. i (t), construct a random matrix according to the identification process of geological disaster remote sensing data, calculate according to the local information of each influencing factor message in each geological disaster remote sensing data and mathematical operations, and obtain the variance of the artificial influencing factor; for the virtual geological disaster interference object, it is difficult to obtain an accurate matrix. With the existence of the random matrix generated in real time, the virtual geological disaster interference object cannot obtain accurate geological disaster information; obtain the variance of the artificial influencing factor, including: the components selected within time t Each influencing factor message i in the disaster remote sensing data is sent to the fusion center, and the artificial influencing factor a is inserted before sending. i (t)Add to A signal is sent over a communication channel.

[0090] Step 3, the fusion center decodes the received signal, and the probability of successful decoding is related to the impact value of the impact factor;

[0091] Step 4: derive the criteria for selection probability and successful decoding probability and obtain the impact factor insertion strategy.

[0092] Step 4: After the fusion of the current geological disaster comprehensive analysis model is completed, the fusion deviation error in the current geological disaster section is calculated, error = real-a, real is the pixel label of the data to be fused, a is the predicted value of the current geological disaster section for the training sample A, and the expression is:

[0093]

[0094] In the formula, is the learning rate, ∑ n_estimators is the cumulative prediction value of the current geological disaster section for training sample A, is the information tree of each influencing factor in the i-th disaster remote sensing data in the current geological disaster area The predicted value on the training set, and update the data value to be fused real = error;

[0095] Step 5: Perform a normality test on the fusion deviation error of the influencing factor image training sample A in the disaster remote sensing data;

[0096] Step 6: When the loss function of the evaluation function on the influencing factor image test sample B in the disaster remote sensing data no longer decreases within the specified number of early evolution stop stages, the training of the geological disaster comprehensive analysis model stops, and the optimal number of iteration stages N of the geological disaster comprehensive analysis model is obtained. The training of the geological disaster comprehensive analysis model is completed, and the process jumps to step 7. Otherwise, the loss function on the influencing factor image test sample B in the disaster remote sensing data continues to decrease, and the process jumps to step 4 to continue the stage iteration.

[0097] The calculation of the loss function on the image test sample B of the influencing factor in the disaster remote sensing data specifically includes:

[0098] Calculate the prediction value of the comprehensive analysis model of geological disasters in the zone of the current geological disaster section on the image test sample B of the influencing factors in the disaster remote sensing data, and the image evolution of each influencing factor in the pre-disaster remote sensing data of the stage j pixel mark in the anti-disaster remote sensing data The expression is:

[0099]

[0100] Where, jud[j] is the judgment variable of the current geological disaster section j; sigmod -1 () is the image evolution of each influencing factor in the sigmod disaster remote sensing data, and the image evolution of each influencing factor in the anti-disaster remote sensing data, boxcos -1 The image evolution of each influencing factor in the BOXCOS disaster remote sensing data is the image evolution of each influencing factor in the anti-disaster remote sensing data; the expression is:

[0101]

[0102] Furthermore, the loss function value of the current geological disaster section is calculated, and the expression is:

[0103] SU=F(a,y B )

[0104] In the formula, y B is the initial pixel label of the test set B for each influencing factor in the disaster remote sensing data, F( ) is the loss function used by the system, which is usually a square loss function for regression problems. In this case, SU = (ay B ) 2 .

[0105] Step 7: Predict the test set of each influencing factor in the disaster remote sensing data. According to the optimal number of iteration stages N of the comprehensive analysis model of geological disasters, the prediction result D of the test set of each influencing factor in the disaster remote sensing data is obtained. The expression is:

[0106]

[0107] In the formula, The image evolution of each influencing factor in the pre-disaster remote sensing data of the pixel labeling in stage j is the image evolution of each influencing factor in the anti-disaster remote sensing data, i is the influencing factor, is the message tree of each influencing factor in the disaster remote sensing data of stage j prediction results.

[0108] In step 2, a normality test is performed on the initial pixel label y of the influencing factor image training sample A in the disaster remote sensing data, including:

[0109] (1) According to the pixel labels of the image training samples of each influencing factor in the disaster remote sensing data, the data distribution discrete bias rate P is calculated, and the expression is:

[0110]

[0111] Where real is the pixel label of the data to be fused, that is, real = y; is the mean value of sample pixel labels, ρ is the sample capacity, and std is the standard deviation of sample pixel labels;

[0112] (2) According to the pixel labels of the image training samples of each influencing factor in the disaster remote sensing data, the data distribution fusion suitable state proportion G is calculated, and the expression is:

[0113]

[0114] (3) Construct the image fusion statistics UT of each influencing factor in the disaster remote sensing data, expressed as:

[0115]

[0116] Test the significance of the UT statistic and examine the degree of bias to determine whether to change the data; if the UT statistic is greater than the threshold, proceed to step (4), otherwise proceed to step (5);

[0117] (4) The data is obviously biased. Determine whether the preset upper limit of the number of data changes is reached. Set the upper limit of the number of changes to max1≤0. If yes, jump to step (5); otherwise, update the value of max1 to make max1=max1-1, perform boxcox transformation on the data, and traverse the b value through the following formula. Select the b that maximizes the likelihood function F of the data pixel mark after the image evolution of each influencing factor in the disaster remote sensing data to generate a change expression:

[0118]

[0119] In the formula, real bis the transformation amount of the disaster remote sensing data in the geological disaster area, ln( ) is the logarithm of the transformation amount, b is the transformation value that conforms to the normal distribution, and the value range is 0.00-1.00;

[0120] Get the new data pixel mark and use it as the first stage fusion pixel mark; set the current geological disaster section judgment variable jud[1] = real;

[0121] (5) If the data bias is not obvious, or the number of image evolutions of each influencing factor in the data pixel mark disaster remote sensing data reaches the target upper limit, the original data pixel mark y is taken as the first stage fusion pixel mark, and the current geological disaster section judgment variable jud[1] = False is set.

[0122] In step 2, the loss function deviation matrix is ​​comprehensively analyzed to develop an insertion method for artificial influence factors, including:

[0123] 2.1, for any initial condition P(t), the fusion mechanism achieves perfect expected fusion when both of the following conditions are met:

[0124]

[0125] Where P e (t) is the deviation of the current geological disaster comprehensive analysis loss function calculated by the virtual geological disaster interference object, H i (t) is a diagonal element, θ i (t) is the matrix null space;

[0126] 2.2, select the artificial impact factor influence value a i (t), using the matrix of the orthogonal basis, we get the matrix null space θ i (t) an orthonormal basis;

[0127] 2.3, Artificial influence factor a i (t) is given by:

[0128] a i (t) = θ i (t)ζ i (t)

[0129] In the formula, ζ i (t) is the component that is the impact factor.

[0130] Through the above embodiments, it can be known that, first of all, the distribution of geological disaster data presents diversity, and the image evolution of each influencing factor in the single formula disaster remote sensing data is difficult to handle a variety of biased situations. In addition, in the process of fusion, the prior art lacks an immediate data correction strategy when there is bias or long tail, and different correction strategies often have relevant requirements for data. If the image evolution of each influencing factor in the corresponding disaster remote sensing data is not carried out, the correction strategy cannot be carried out. The image evolution of each influencing factor in the corresponding disaster remote sensing data often causes forced mapping of the data, which destroys the inherent statistical law of the data, affects the fusion ability of the subsequent algorithm, and cannot improve the prediction accuracy. How to reasonably correct the data distribution and ensure the fusion ability of the algorithm is crucial. The present invention establishes the next message tree of each influencing factor in the disaster remote sensing data based on the deviation direction of the fusion loss function. The process of establishing the message tree group of each influencing factor in the entire disaster remote sensing data is a tightly coupled process. Changing the fusion deviation at a certain stage will affect the establishment of each influencing factor message tree in all subsequent disaster remote sensing data. How to achieve the purpose of flexibly adjusting the distribution of data pixel labels through patterned decoupling, and alleviate the accumulation of bias to ensure the algorithm fusion effect and improve the prediction accuracy.

[0131] The present invention fully alleviates the impact of data bias on the algorithm system through the evolution of the image of each influencing factor in the disaster remote sensing data through multi-stage pixel labeling, so that the algorithm can dynamically adjust the deviation during the iteration process, improve the flexibility of the algorithm, and improve the fusion ability of the algorithm. The present invention minimizes the impact of data bias on the algorithm, improves the flexibility of the algorithm, and improves the fusion ability of the algorithm.

[0132] In the process of alleviating the bias of the deviation, the present invention aims at the requirement of data positivity of the image evolution of each influencing factor in the disaster remote sensing data. In the multi-stage fusion process, the image evolution of each influencing factor in the disaster remote sensing data is first performed to compress the deviation to an interval according to the stage fusion deviation, thereby alleviating the bias of the data and ensuring the positivity of the data, and further enhancing the nonlinear expression ability of the algorithm. Subsequently, the image evolution of each influencing factor in the disaster remote sensing data is performed to better alleviate the bias of the data. The present invention overcomes the risk brought by over-fusion by introducing a timely stopping strategy. At the same time, the timely stopping strategy with stage as the granularity better avoids the influence of special values ​​than the traditional timely stopping strategy with the message tree of each influencing factor in the disaster remote sensing data as the granularity, thereby making the algorithm more robust.

[0133] The method of the present invention has greatly improved the system performance on the test sets of various influencing factors in disaster remote sensing data in multiple time windows. The fusion ability of the algorithm under biased data is greatly enhanced, and at the same time it shows good robustness, which effectively solves the adaptability problem of the algorithm under biased data conditions.

[0134] Embodiment 2, as Figure 2 As shown, an embodiment of the present invention provides a geological disaster analysis system based on remote sensing data fusion, the system comprising:

[0135] The influencing factor data information acquisition module 1 is used to obtain the influencing factor data information in the disaster remote sensing data, and the influencing factor data information includes collapse, landslide, mud-rock flow, ground fissure, soil erosion, land desertification and swamping, soil salinization, and earthquake, volcano, geothermal damage image information;

[0136] The multi-influencing factor fusion module 2 is used to obtain a geological disaster comprehensive analysis model and a geological disaster comprehensive analysis image by using a multi-influencing factor fusion method based on the influencing factor data information in the acquired disaster remote sensing data;

[0137] The module 3 for acquiring comprehensive characteristics of the area affected by the disaster is used to acquire comprehensive characteristics of the area affected by the disaster based on the comprehensive analysis image of geological disasters, and to visualize the characteristics; the comprehensive characteristics of the area include the characteristics of surface landform changes in the area affected by the disaster.

[0138] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0139] Since the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present invention, their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0140] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated in one processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment.

[0141] An embodiment of the present invention further provides a computer device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor implements the steps in any of the above-mentioned method embodiments when executing the computer program.

[0142] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0143] An embodiment of the present invention further provides an information data processing terminal, which, when executed on an electronic device, provides a user input interface to implement the steps in the above method embodiments. The information data processing terminal is not limited to mobile phones, computers, and switches.

[0144] An embodiment of the present invention further provides a server, which is used to provide a user input interface to implement the steps in the above method embodiments when executed on an electronic device.

[0145] An embodiment of the present invention provides a computer program product. When the computer program product runs on an electronic device, the electronic device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0146] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the camera / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0147] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered within the protection scope of the present invention.

Claims

1. A geological disaster analysis method based on remote sensing data fusion, characterized in that: The method includes: S1: Obtaining the influencing factor data information in the disaster remote sensing data, which includes: collapse, landslide, mud-rock flow, ground fissure, soil erosion, land desertification, swamping, soil salinization, as well as earthquake, volcano, geothermal damage image information; S2: Based on the acquired influencing factor data information, a comprehensive analysis model of geological hazards is obtained using a multi-influencing factor fusion method, and a comprehensive analysis image of geological hazards is obtained; S3, based on the comprehensive analysis image of geological disasters, obtain the comprehensive characteristics of the area affected by the disaster and visualize them; the comprehensive characteristics of the area include the surface landform change characteristics of the area affected by the disaster.

2. The geological disaster analysis method based on remote sensing data fusion according to claim 1 is characterized in that: In step S2, a comprehensive analysis model of geological disasters is obtained by using a multi-influencing factor fusion method, and a comprehensive analysis image of geological disasters is obtained, including: Step 1: Input the image training sample A of the influencing factors in the disaster remote sensing data and the image testing sample B of the influencing factors in the disaster remote sensing data, which are used for the training of the geological disaster comprehensive analysis model and the evaluation of the geological disaster comprehensive analysis model respectively; Step 2: Perform a normality test on the initial pixel label y of the influencing factor image training sample A in the disaster remote sensing data; Step 3: According to the number of message trees N of each influencing factor in the current geological disaster zone disaster remote sensing data zone , using mark as the fusion pixel marker, establish the message tree of each influencing factor in each disaster remote sensing data Integrate the loss function deviation of the current geological disaster comprehensive analysis model; Step 4: After the fusion of the current geological disaster comprehensive analysis model is completed, the fusion deviation error in the current geological disaster section is calculated, error = real-a, real is the pixel label of the data to be fused, a is the predicted value of the current geological disaster section for the training sample A, and the expression is: In the formula, is the learning rate, ∑ n_estimators is the cumulative prediction value of the current geological disaster section for training sample A, is the information tree of each influencing factor in the i-th disaster remote sensing data in the current geological disaster area The predicted value on the training set, and update the data value to be fused real = error; Step 5: Perform a normality test on the fusion deviation error of the influencing factor image training sample A in the disaster remote sensing data; Step 6: When the loss function of the evaluation function on the influencing factor image test sample B in the disaster remote sensing data no longer decreases within the specified number of early evolution stop stages, the training of the geological disaster comprehensive analysis model stops, and the optimal number of iteration stages N of the geological disaster comprehensive analysis model is obtained. The training of the geological disaster comprehensive analysis model is completed, and the process jumps to step 7. Otherwise, the loss function on the influencing factor image test sample B in the disaster remote sensing data continues to decrease, and the process jumps to step 4 to continue the stage iteration. Step 7: Predict the test set of each influencing factor in the disaster remote sensing data. According to the optimal number of iteration stages N of the comprehensive analysis model of geological disasters, the prediction result D of the test set of each influencing factor in the disaster remote sensing data is obtained. The expression is: In the formula, The image evolution of each influencing factor in the pre-disaster remote sensing data of the pixel labeling in stage j is the image evolution of each influencing factor in the anti-disaster remote sensing data, i is the influencing factor, is the message tree of each influencing factor in the disaster remote sensing data of stage j prediction results.

3. The geological disaster analysis method based on remote sensing data fusion according to claim 2 is characterized in that: In step 2, a normality test is performed on the initial pixel label y of the influencing factor image training sample A in the disaster remote sensing data, including: (1) According to the pixel labels of the image training samples of each influencing factor in the disaster remote sensing data, the data distribution discrete bias rate P is calculated, and the expression is: Where real is the pixel label of the data to be fused, that is, real = y; is the mean value of sample pixel labels, ρ is the sample capacity, and std is the standard deviation of sample pixel labels; (2) According to the pixel labels of the image training samples of each influencing factor in the disaster remote sensing data, the data distribution fusion suitable state proportion G is calculated, and the expression is: (3) Construct the image fusion statistics UT of each influencing factor in the disaster remote sensing data, expressed as: Test the significance of the UT statistic and examine the degree of bias to determine whether to change the data; if the UT statistic is greater than the threshold, proceed to step (4), otherwise proceed to step (5); (4) The data is obviously biased. Determine whether the preset upper limit of the number of data changes is reached. Set the upper limit of the number of changes to max1≤0. If yes, jump to step (5); otherwise, update the value of max1 to make max1=max1-1, perform boxcox transformation on the data, and traverse the b value through the following formula. Select the b that maximizes the likelihood function F of the data pixel mark after the image evolution of each influencing factor in the disaster remote sensing data to generate a change expression: In the formula, real b is the transformation amount of the disaster remote sensing data in the geological disaster area, ln( ) is the logarithm of the transformation amount, b is the transformation value that conforms to the normal distribution, and the value range is 0.00-1.00; Get the new data pixel mark and use it as the first stage fusion pixel mark; set the current geological disaster section judgment variable jud[1] = real; (5) If the data bias is not obvious, or the number of image evolutions of each influencing factor in the data pixel mark disaster remote sensing data reaches the target upper limit, the original data pixel mark y is taken as the first stage fusion pixel mark, and the current geological disaster section judgment variable jud[1] = False is set.

4. The geological disaster analysis method based on remote sensing data fusion according to claim 2 is characterized in that: In step 3, the loss function deviation of the current geological disaster comprehensive analysis model is integrated, including: Step 1, collect information about each influencing factor in the disaster remote sensing data to the fusion center through the communication network; Step 2, based on the physical process and current geological hazards, the loss function deviation matrix is ​​comprehensively analyzed to develop an insertion method for artificial influence factors, so that the fusion of the virtual geological hazard interference object and the current geological hazard comprehensive analysis loss function deviation become worse; Step 3, the fusion center decodes the received signal, and the probability of successful decoding is related to the impact value of the impact factor; Step 4: derive the criteria for selection probability and successful decoding probability and obtain the impact factor insertion strategy.

5. The geological disaster analysis method based on remote sensing data fusion according to claim 4 is characterized in that: In step 2, the method for inserting the artificial influence factor includes: For objects without geological hazards, P is calculated based on the parameter information of the dynamic model and observation equation. i (t), construct a random matrix according to the identification process of geological disaster remote sensing data, calculate according to the local information of each influencing factor message in each geological disaster remote sensing data and mathematical operation, and obtain the variance of the artificial influencing factor; It is difficult to obtain an accurate matrix for virtual geological disaster interference objects. Due to the existence of random matrices generated in real time, virtual geological disaster interference objects cannot obtain accurate geological disaster information.

6. The method for analyzing geological hazards based on remote sensing data fusion according to claim 5, characterized in that: Get the variance of artificial influence factors, including: The components selected at time t Each influencing factor message i in the disaster remote sensing data is sent to the fusion center, and the artificial influencing factor a is inserted before sending. i (t)Add to A signal is sent over a communication channel.

7. The method for analyzing geological hazards based on remote sensing data fusion according to claim 4, characterized in that: In step 2, the loss function deviation matrix is ​​comprehensively analyzed to develop an insertion method for artificial influence factors, including: 2.1, for any initial condition P(t), the fusion mechanism achieves perfect expected fusion when both of the following conditions are met: Where P e (t) is the deviation of the current geological disaster comprehensive analysis loss function calculated by the virtual geological disaster interference object, H i (t) is a diagonal element, θ i (t) is the matrix null space; 2.2, select the artificial impact factor influence value a i (t), using the matrix of the orthogonal basis, we get the matrix null space θ i (t) an orthonormal basis; 2.3, Artificial influence factor a i (t) is given by: a i (t)=θ i (t)ζ i (t) In the formula, ζ i (t) is the component that is the impact factor.

8. The method for analyzing geological hazards based on remote sensing data fusion according to claim 2, characterized in that: In step 6, the calculation of the loss function on the impact factor image test sample B in the disaster remote sensing data specifically includes: Calculate the prediction value of the comprehensive analysis model of geological disasters in the zone of the current geological disaster section on the image test sample B of the influencing factors in the disaster remote sensing data, and the image evolution of each influencing factor in the pre-disaster remote sensing data of the stage j pixel mark in the anti-disaster remote sensing data The expression is: Where, jud[j] is the judgment variable of the current geological disaster section j; sigmod -1 ( ) is the image evolution of each influencing factor in the sigmod disaster remote sensing data and the image evolution of each influencing factor in the anti-disaster remote sensing data, boxcos -1 The image evolution of each influencing factor in the BOXCOS disaster remote sensing data is the image evolution of each influencing factor in the anti-disaster remote sensing data; the expression is:

9. The method for analyzing geological hazards based on remote sensing data fusion according to claim 8, characterized in that: Calculate the loss function value of the current geological disaster section, the expression is: SU=F(a,y B ) In the formula, y B is the initial pixel label of the test set B of each influencing factor in the disaster remote sensing data, F( ) is the loss function used by the system, which is usually a square loss function for regression problems. In this case, SU = (ay B ) 2 .

10. A geological disaster analysis system based on remote sensing data fusion, characterized in that: The system is implemented by the geological disaster analysis method based on remote sensing data fusion according to any one of claims 1 to 9, and the system comprises: An influencing factor data information acquisition module (1) is used to acquire influencing factor data information in disaster remote sensing data, wherein the influencing factor data information includes collapse, landslide, mud-rock flow, ground fissure, soil erosion, land desertification and swamping, soil salinization, and earthquake, volcano, and geothermal damage image information; A multi-influencing factor fusion module (2) is used to obtain a geological disaster comprehensive analysis model and a geological disaster comprehensive analysis image based on the influencing factor data information in the acquired disaster remote sensing data by using a multi-influencing factor fusion method; The module (3) for acquiring comprehensive characteristics of the area affected by the disaster is used to acquire comprehensive characteristics of the area affected by the disaster based on the comprehensive analysis image of the geological disaster, and to visualize the characteristics; the comprehensive characteristics of the area include the characteristics of the surface landform changes in the area affected by the disaster.

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