WO2006120724A1 - Systeme d’informations geographiques utilisant des reseaux neutres - Google Patents

Systeme d’informations geographiques utilisant des reseaux neutres Download PDF

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Publication number
WO2006120724A1
WO2006120724A1 PCT/JP2005/008310 JP2005008310W WO2006120724A1 WO 2006120724 A1 WO2006120724 A1 WO 2006120724A1 JP 2005008310 W JP2005008310 W JP 2005008310W WO 2006120724 A1 WO2006120724 A1 WO 2006120724A1
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data
input
geographic information
teacher
information system
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PCT/JP2005/008310
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English (en)
Japanese (ja)
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Kohei Arai
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Saga University
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Priority to PCT/JP2005/008310 priority Critical patent/WO2006120724A1/fr
Priority to JP2007526717A priority patent/JP4719893B2/ja
Publication of WO2006120724A1 publication Critical patent/WO2006120724A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks

Definitions

  • the present invention relates to a geographic information system that accumulates and utilizes spatial data, and more particularly to a geographic information system to which a neural network is applied.
  • SST estimation has been performed using satellite observational data.
  • Various methods have been proposed as methods for estimating SST.
  • Thermal infrared radiometers have been mainly used in SST. This is because the atmospheric influence is small in the observation wavelength band.
  • the observed luminance Ii in the i wavelength band sensitive to the thermal infrared wavelength range is expressed by the following equation (1).
  • equation (1) there is a method of estimating SST by solving the inverse problem of finding the earth surface temperature Ts that is the factor from the observed luminance Ii.
  • the radiation transfer equation (1) is essentially non-linear. If it is a linear inverse problem, there are methods such as least squares method and orthogonal expansion and solving to prevent the divergence of the solution and solve the inverse problem.
  • the solution of the nonlinear inverse problem is limited either by linearization with a limited range of solution or solving iteratively. Therefore, the calculation for one data is very complicated and the amount of calculation increases. It is too difficult to use the method of solving this equation (1) for the huge amount of data in all sea area every time. [Calculation method for sea surface temperature (SST) estimation: Split-Window method]
  • SW method Split-Window method
  • the earth surface temperature is the same.
  • the influence of the atmosphere can be obtained from the difference in the observed brightness temperature in each wavelength band.
  • Patent Document 1 Japanese Patent Application Laid-Open No. 2005-52045
  • the present invention has been made to solve the above-mentioned problems, and has observation data and parameters for SST estimation having geographical information as spatial data, while reducing the amount of calculation and errors.
  • the purpose is to provide a geographic information system that can carry out estimation widely.
  • the present invention is not limited to the implementation of SST estimation, and it is another object of the present invention to be able to extensively implement other estimation using spatial data other than spatial data for SST estimation. Means to solve the problem
  • the regression equation of the SW method is for obtaining an output from the correlation of a plurality of input data.
  • NN -Uural network
  • BP backpropagation
  • the NN learns to obtain an empirically low error solution by giving repetitive data.
  • measured values at sea level and satellite data force also determine parameters.
  • BP can correct the internal parameters automatically by comparing it with the estimated value from the satellite data, using the measured value on the sea surface as the teaching data.
  • the introduction of NN can improve accuracy and automate.
  • GIS enables optimization of parameters.
  • SST is estimated locally and depends on climate, longitude and latitude. Conversely, if geographical information such as climate, latitude, and longitude matches, parameters can be handled collectively. By using appropriate parameters, it is possible to improve the SST estimation of other sea areas. Therefore, it is required to create a database suitable for integrated search and management of geographical information.
  • a geographic information system using a eural network includes a recording unit for recording spatial data, which is data having information on position, and analysis means for analyzing the spatial data of the recording unit.
  • analysis means input data of the teacher set is read into the input layer, forward calculation is performed, and backward calculation is performed by using the output data of the output layer of the calculation result and the teacher data of the teacher set to learn.
  • a teacher set is given and learned, and after constructing a eural network, input data is input and estimated, so in the geographic information system, a large amount of spatial data is generated.
  • the force user can input and learn a teacher set that does not apply the algorithm of the estimation equation, so that subsequent estimation can be performed and analysis results can be easily obtained.
  • the input data is recorded in the recording unit of the geographic information system, and the output data is recorded in the same recording unit, thereby constructing a system in which the neural network and the geographical information system are integrated integrally, It enables quick analysis.
  • the present invention can obtain an estimation result quickly as a simple algorithm.
  • the geographic information system using the eural network is provided with a spatio-temporal search means for performing a spatio-temporal search as necessary, and estimation of a certain time of a certain position is performed at the same time before the last year. It is estimated using weights. As described above, in the present invention, it is searched whether or not there is a weight at the same position and the same position as the position designated by the space-time search means, and if there is a matching weight, the weight used is used. In this way, estimation can be performed without learning. Weights are sometimes called combined load, load .
  • “before last year” means before “last year” and is used in the sense including last year
  • the geographic information system using the eural network is provided with similar area detection means for detecting an area meeting the detection condition specified by the user as the similar area, if necessary,
  • the target data of the condition is recorded as spatial data, and when a part of the similar area has already been learned, it has already been learned and has already been constructed-using the eural network and the other in the similar area It is to estimate for the part.
  • the similar area detection means since the similar area detection means detects a similar area that matches the detection condition, if a part of the similar area has already been learned, learning is performed in other similar areas. It can be estimated.
  • the geographic information system using the eural network can adjust the detection condition for detecting the similar area as needed.
  • the detection condition for detecting the similar area can be adjusted, and the user can freely adjust the similar area and the accuracy. That is, if the accuracy is not required, the detection condition is relaxed and the similarity region is expanded, and if the accuracy is required, the detection condition is strict and the similarity region is reduced.
  • the detection condition is "an area 50 to 70 km from land, the Kuroshio or the Tsushima Current, the latitude 20 to 50, and the longitude 120 to 150". If there is a problem, it is possible to adjust by changing the value of the numerical condition or eliminating the condition itself in cases other than the numerical condition. Make adjustments easy In order to present the user with similar areas of detection results on the display unit, it is preferable to perform layer display to facilitate comparison with similar areas detected earlier. .
  • the geographic information system using the eural network detects at least one sample point for each of the obtained similar areas, receives the input of the teacher set for the sample points, and inputs the input teacher set.
  • Data has already been constructed-reasoning is performed using a eural network, output data is compared with teacher data of a teacher set, and the power can be regarded as a similar area depending on whether the error is within a threshold or not.
  • the sample point is detected for the area detected as the similar area only by detecting the similar area based on the detection condition set by the user, and the teacher set for the carious sample points
  • the user is requested, the user infers the input data of the teacher set inputted in response to the request, inference is made using the existing -Uural network, the output data is compared with the teacher data, and the error is within the threshold. If so, it is regarded as a similar area, so eligibility is confirmed as a similar area, and it is possible to prevent inference for false similar areas
  • a geographic information system using a eural network includes: a storage unit for recording, as spatial data, a far-infrared image corresponding to a position obtained by remote sensing by an artificial satellite;
  • the analysis means is provided with an analysis means for analyzing the spatial data, and the analysis means gives a far infrared image of the position at the input layer, performs forward calculation, and outputs the sea surface temperature of the output layer and the sea surface temperature as teaching data. It is constructed for each position by performing backward calculation by learning-A far-infrared image is input to the input layer of the Euler network, and the sea surface temperature of the input far-infrared image is determined.
  • the far-infrared image is recorded as spatial data in the recording unit, the analysis unit learns using the teacher set, and the far-infrared image is input.
  • An appropriate estimate can be made using the constructed-Eural network to determine the sea surface temperature corresponding to the input far-infrared image.
  • the geographic information system using the eural network includes two far infrared images at the same position in different frequency bands, and the same teacher data as the far infrared image. It is the measured data or the satellite observation data of the sea surface temperature at the location. As described above, in the present invention, since the sea surface temperature is determined using different far-infrared images of frequency bands at the same position, the temperature can be determined more accurately.
  • the geographic information system using the eural network can, if necessary, determine the position already learned when there is a position where the user specified position and the detection condition match.
  • the sea surface temperature is determined using the estimation.
  • the neural network has been trained in the other position where the corresponding position and the detection condition match. In the case where C is constructed, learning is not performed directly for the specified position. Since the sea surface temperature is estimated using the Euler network at other positions where the detection conditions are met, unnecessary learning Can quickly estimate the sea surface temperature
  • a method applied to a geographic information system using a eural network according to the present invention is applied to a geographic information system that analyzes spatial data of a recording unit that records spatial data, which is data having information on position.
  • the process of reading the input data of the teacher set in the input layer and performing forward calculation, and the process of the process of causing the forward calculation, performs backward calculation using the output data of the output layer and the teacher data of the teacher set. And the step of reading the input data into the input layer after the step of learning to obtain an estimation result.
  • the geographic information program using the eural network comprises: a processor for storing spatial data of a recording unit for recording spatial data which is data having information on position; A geographic information program that analyzes data, in which the processor reads the input data of the teacher set in the input layer and makes forward calculation, and the output of the output layer is the calculation result of the procedure in which the processor makes the forward calculation.
  • a procedure for performing backward calculation and learning based on the data and the teacher data of the teacher set, and a procedure for reading the input data to the input layer after the learning procedure and for obtaining an estimation result are executed.
  • FIG. 1 is a block diagram of a geographic information system according to a first embodiment of the present invention.
  • FIG. 2 is a diagram of a euron model according to the first embodiment of the present invention.
  • FIG. 3 is a structural diagram of a hierarchical-eural network according to the first embodiment of the present invention.
  • FIG. 4 is a structural diagram of an interconnection-eural network according to the first embodiment of the present invention.
  • FIG. 5 is a structural view of a perceptron according to the first embodiment of the present invention.
  • FIG. 6 is an explanatory view of forward calculation of back propagation according to the first embodiment of the present invention.
  • FIG. 7 is an explanatory diagram of backward calculation of back propagation according to the first embodiment of the present invention.
  • FIG. 8 is a processing image diagram of SST estimation by the geographic information system according to the first embodiment of the present invention.
  • FIG. 9 is an explanatory diagram of a geographic information system according to the first embodiment of the present invention.
  • FIG. 10 is an operation flowchart of the geographic information system according to the first embodiment of the present invention.
  • FIG. 11 is an operation flowchart of the geographic information system according to the first embodiment of the present invention.
  • FIG. 12 is an explanatory diagram of similar area detection in the geographic information system according to the second embodiment of the present invention.
  • FIG. 13 This is an observation image of channel 4 at 17:11 on May 20, 2002.
  • FIG. 14 This is an observation image of channel 5 at 17:11 on May 20, 2002.
  • FIG. 23 is a program list of modules of the neural network according to the embodiment.
  • FIG. 24 is a program list of modules of the neural network according to the embodiment.
  • FIG. 25 is a program list of modules of the neural network according to the embodiment.
  • FIG. 26 is a program list of modules of the neural network according to the embodiment.
  • FIG. 27 is a program list of modules of the neural network according to the embodiment.
  • FIG. 28 is a program list of modules of the neural network according to the embodiment.
  • FIG. 29 is a program list of modules of the neural network according to the embodiment. Explanation of sign
  • the present invention can also be implemented as a computer usable program and method. Also, the present invention can be implemented in hardware, software, or software and hardware embodiments.
  • the program can be recorded on any computer readable medium such as a hard disk, a CD-ROM, a DVD-ROM, an optical storage device or a magnetic storage device.
  • programs can be recorded on other computers via a network
  • the geographic information system according to the first embodiment of the present invention will be described with reference to the drawings.
  • the geographic information system according to the present embodiment in FIG. 1 is a far infrared image corresponding to a position obtained by remote sensing by artificial satellites.
  • a database 10 for recording spatial data an analysis means 23 for analyzing the spatial data in the database 10, and a search means 22 for searching the spatial data. It is constructed for each position by providing an infrared image, performing forward calculation, and performing backward calculation and learning from the sea surface temperature of the output layer of the calculation result and the sea surface temperature serving as teacher data. Then, the far-infrared image is input, and the sea surface temperature of the input far-infrared image is obtained.
  • the control unit 20 reads spatial data from the database 10 through the user's operation of the input unit 40 in addition to the search means 22 and the analysis means 23 in addition to the search means and the analysis means 23. It has display means 21 for displaying.
  • the search result of the search means 22 may be displayed on the display unit 30 by the search means 22 itself, or may be displayed by passing the search result to the display means 21.
  • the analysis means 23 is also the same.
  • the search means 22 can perform attribute search and space search.
  • Attribute search is a search for attribute data in spatial data, and the search expression specified by the user by operating the input unit 40 differs depending on the attribute.
  • Spatial search means map data other than attribute data in spatial data Search for etc.
  • attribute search and spatial search can both be performed in terms of time, and in particular, the search performed in consideration of time in spatial search is called space-time search. In the geographic information system, these attribute search, space search and space-time search are well known techniques and can be applied to the system as needed by those skilled in the art.
  • the analysis means 23 can perform vector overlay analysis, point-in-polygon analysis, knocking, geographical measurement, point operation, neighborhood operation, topographic analysis, network analysis, Borony division and the like.
  • Vector overlay analysis is an analysis that performs polygon processing on geometric elements of points, lines, and faces.
  • Point-in-polygon analysis is processing to determine whether the points that make up a line or surface are included in the interior of the polygon. Knocking is about creating the required zones around a specific feature. Geographical measurements are distance measurement, area measurement, and volume measurement, which measure Euclidean distance according to the Pythagorean theorem.
  • Point operations perform mathematical operations (addition operation, multiplication operation, maximum value operation) on the attribute values of cells in the same position.
  • the neighbor operation is to perform various operations on the neighboring cells of one cell and the active cell.
  • the Boronoli division is to divide the area in the sphere of power.
  • the database 10 records, as spatial data, input data of the input layer, intermediate data of the intermediate layer, output data of the output layer, and a teacher set. Therefore, these data can also be searched and analyzed in geographic information systems. In addition, it is possible to achieve excellent performance by recording data in the database 10 in the system rather than inputting data from the outside as external data.
  • the SW method requires a plurality of thermal infrared observation wavelength band data.
  • NOAA meteorological observation satellite
  • NOAA United States Ocean Atmosphere Administration
  • NOAA12, NOAA14, NOAA15, NOAA16, NOAA17 There are five, NOAA12, NOAA14, NOAA15, NOAA16, NOAA17, which are used for various earth observation tasks.
  • the optical sensor mounted on NOAA is AVHRR (Improved High Resolution Radiometer). Its performance is: resolution l.lkm, observation width 2800km, number of pixels 2048 pixels / line, density gradation 10 bits.
  • the observation wavelength bands of each channel and the main observation targets are as shown in Table 1 below. With NOAA12 NOAA 14 does not have channel 3A, NOAA 15 NOAA 16 NOAA 17 is a channel
  • channels 4 and 5 are used in the present invention.
  • a neural network is a model that is modeled on neurons in an organism.
  • the junction between a neuron and another neuron is called a synapse.
  • Each-Euron learns by linking each neuron by synapse and updating the synapse connection weight.
  • the feature is that NN learns to automatically output appropriate output.
  • the solution method is learned empirically.
  • non-linear function net which also has a net value and a threshold repulsion, there are various forces in the function net). Most of the forces have saturation characteristics so that the value of the output y falls within a certain range.
  • BP Back-propagation
  • BP Back-propagation
  • a continuous monotonically increasing sigmoid function is used.
  • a logarithmic function (equation 4) is used as the sigmoid function.
  • the synapse learning is performed based on Heb's learning rule "When cell A is excited, if cell B is always excited, the synaptic weight from cell A to B is large.”
  • E is a function of wi.
  • Learning neural networks is equivalent to finding wi so that the error E is minimized. Therefore, let the small positive constant 7? Be a learning coefficient, and let the correction value Awi of the connection weight be the following equation 6.
  • Equations 6 and 8 are called Generalized Delta Rules.
  • NN neural networks
  • FIGS. 3 and 4 classification of neural networks (NN) and various features will be described (see FIGS. 3 and 4).
  • NNs There are several types of NNs that have been repeatedly improved. Depending on the type of NN, there is a problem suited to the solution method.
  • a back propagation neural network is suitable for the SW method and the present invention which handles a large amount of data.
  • Each NN is divided into two according to the following four features.
  • teacher data Correct data to be compared with output data at the time of learning is called teacher data. Depending on whether teacher data is prepared, it is classified as having a teacher signal and without a teacher signal.
  • NNs each of which includes N.Euron in each layer and processes each layer, is called a hierarchical-eural network. Without being divided into each layer-von Eun is linked so that the NN is called an interconnected-Eural network.
  • An NN that flows data forward only is called a feed-forward-user network. Furthermore, NN that flows data backward is called a feedback-type eural network.
  • the analog type is not used so much at present, and the mainstream is digital type.
  • perceptron will be described.
  • Has a teacher signal performs hierarchical, feedforward processing. As shown in Fig. 5, it consists of an input layer, multiple intermediate layers (hidden layers), and an output layer. The input data propagates -Eron of each layer and the output is finally determined. If there is a teacher signal, the output layer-Eron updates the synapse coupling weight, comparing with the output. The synapse learning is only the output layer-Euron, which is in direct contact with the teacher signal, and learning is not performed in the middle layer. Therefore, even if learning is repeated, nonlinear problems can not be solved.
  • knock propagation BP
  • the error back propagation algorithm is incorporated into the perceptron and the feedback type is improved.
  • the operation for input is the same as perceptron, and it is called by BP as forward operation (see Fig. 6).
  • the learning of the newly made middle layer and input layer-euros is performed from the output layer toward the input layer, and is called backward calculation (see Fig. 7). It requires a lot of input and output data for learning. It is suitable for pattern recognition and can solve nonlinear higher-order problems.
  • BP knock propagation
  • a teacher signal corresponding to the input vector is provided to the output layer. And learning of the weight by error reverse propagation is performed.
  • K be the number of euros in the output layer, and some be ⁇ ⁇ ⁇ k and the output ok and the teacher signal tk.
  • the output of the output layer, the teacher signal and the error sum are defined as the following equation.
  • Equation 11 [Equation 11] ⁇ ,: ⁇ : ⁇ :: 'u' 3 ⁇ 4 (output! G of a neuron's force fn) ' ⁇ ⁇ ⁇ (1)) change in synaptic weight between the middle layer and the output layer According to the general delta delta rule (Equation 8), the quantity is as shown in Equation 12.
  • GIS Geographic Information System
  • GIS uses maps based on specific themes called thematic maps, organizes data from each area, and extracts quantitative information on phenomena from multiple thematic maps (see Figures 8 and 9).
  • GIS Geographic Information System
  • thematic maps deal only with the water temperature distribution for each region, and remote sensing data from satellites deal with far infrared images.
  • the database systematically organizes and stores the stored data centrally and makes it available from programs. Link to other databases to enable complex analysis and processing.
  • a digital representation of information about features is called spatial data. All spatial data carry some form of spatial location information.
  • Attribute data can also include photos and videos, etc. that are not powerful in text or numeric format.
  • ID In order to associate attribute data with graphic data, a symbol generally called an ID is used.
  • drawing table ID, left corner origin coordinates
  • layer table layer ID, layer name
  • sea surface temperature ID, layer, sea surface temperature, coordinates
  • ocean current It is a schema of ID, layer ID, ocean current name, coordinates
  • coast ID, layer ID, chain coordinates.
  • the schemas listed here are merely examples, and persons skilled in the field of geographic information systems can appropriately modify, add, and delete as appropriate.
  • attribute data is referenced, aggregated, and various analyzes are performed via ID. This time, when dealing with thematic maps in GIS, the overlapping latitude and longitude in the spatial method will be dealt with in an overlapping manner.
  • parameters for each sea area are stored.
  • normal display is performed, for example, as follows.
  • the user inputs the keyboard, mouse, etc. 40
  • a required point When displaying a designated point (hereinafter referred to as a required point) by using, first, the coordinates of the required point on the display means 21 are obtained, and the drawing segment ID (required drawing segment) to which the force coordinate belongs is specified.
  • the map data and attribute data relating to the required drawing section, and further, the map data and attribute data relating to the adjacent drawing section of the required drawing section are also read out from the database 10 and stored in the buffer.
  • the operation of the Euler network function which is a feature of the present invention, is initially performed by the user using the input unit 40 to designate an estimated position (see FIG. 10, step 101).
  • the analysis means 23 determines whether or not learning has been performed for the designated estimated position (step 102). If it is determined that learning has not been completed, a request for a learning set is issued to the user (step 103). The user specifies the learning set as required (step 104). When the designation of the learning set from the user is received, the analysis means 23 performs defined processing learning using the powerful learning set as input data (step 200).
  • step 112 If it is determined in step 102 that learning has been completed, or after step 200, a request for observation data is made to the user (step 111). In response to this request, the user specifies observation data (step 112). After receiving the designation of observation data, the analysis means 23 carries out estimation, which is a defined process, using the learned two-eural network (step 300). After estimation, the estimation result is displayed on the display unit 30 (step 121).
  • learning step 200
  • observation data of the learning set is read (see FIG. 11, step 201 below), and learning data is read (step 202). The product of the read observation data and the weight of the input layer and the middle layer is calculated (step 203).
  • Operation of the sigmoid function is performed with the result of the product operation as an argument (step 204).
  • the product of this operation result and the weight of the intermediate layer and the output layer is calculated (step 205).
  • the operation of the sigmoid function is performed using the result of the product operation as an argument (step 206).
  • the calculation result output value of the output layer
  • the output value of the intermediate layer, and the teacher data as arguments
  • the amount of change in load between the intermediate layer and the output layer is calculated (step 207).
  • the amount of change in load between the input layer and the intermediate layer is calculated (step 208).
  • step 207 and step 208 the weights of the new intermediate layer and output layer, and the weights of the input layer and intermediate layer can be obtained and reflected. It is determined whether or not the teacher set is the last (step 209), and if it is the last, the defined processing learning is ended. If it is determined in step 209 that it is not the last one, the process moves to step 201.
  • the predefined process estimation (step 300) is performed through steps 201 to 206.
  • learning is performed and learning is performed by giving a set of teachers that is not limited to the functions of the usual geographic information system using display, analysis, and search using spatial data.
  • excellent SST estimation can be performed on the geographic information system, and more rapid estimation can be performed.
  • image data related to SST can be displayed on the geographical information system, and comparison with the estimation results can be easily performed.
  • input data in the input layer, intermediate data in the intermediate layer, output data in the output layer, and a teacher set are recorded in the database 10, which enables rapid learning and estimation.
  • the observation data used for estimation and the teacher set used for learning may be a set of numerical data or image data.
  • image data it is converted into numerical data by a predetermined conversion formula and used in estimation and learning.
  • the estimation of SST in remote sensing-force applying the Euler network geographic information system The present invention is not limited to this estimation, and can be applied to other remote sensing, and used for analysis other than remote sensing. You can also For example, it can be applied to vegetation, chemical content in the atmosphere, and the like. However, there must be a relationship between the input data for estimation and the estimation results. For example, the present invention can not be applied because it has nothing to do with SST and population density. Whether there is a relationship or not can be found by heuristics, or analysis may find some relationship. In general, if the relationship is strong, even one intermediate layer may require multiple intermediate layers if the relationship is weak.
  • the number of nodes changes according to a force estimation method in which the nodes of the input layer are 2 nodes, the nodes of the intermediate layer are 2 and the nodes of the output layer are 1.
  • the node of the input layer may be 1
  • the node of the middle layer may be 1
  • the node of the output layer may be 1.
  • the intermediate layer is not limited to one layer, and may have a multilayer structure.
  • the layer of the geographic information system and the layer of the neural network can be regarded as the same one, and the database as the graphic data or attribute data of the geographic information system without discarding the output value of the middle layer. It can also be recorded in 10. Also, a set of weights can be treated as a layer. The change of weight itself can be analyzed.
  • the estimation result refers only to numerical data after numerical calculation, but the numerical data is visualized and displayed on the display unit 30 so that the user can compare and evaluate more visually. It can also be displayed.
  • a geographic information system according to a second embodiment of the present invention will be described.
  • This embodiment The geographic information system according to is configured in the same manner as the first embodiment, and is configured to include a similar area detection unit that detects a similar area that is an area that can be estimated using the weight of the same-eural network.
  • the operation of the similar area detection means is started by the user designating the learned area and instructing the detection of the similar area through the input unit 40.
  • An attribute search or a spatial search is performed for a part having the same area as the designated area and the same area from the land (the search means 22 may be used for the search here).
  • the similar area detection means outputs the area as the search result as the similar area and displays it on the display unit 30.
  • the strongly similar area already learned-since the Euranole network can be used, learning that inputs the learning set is unnecessary, and estimation is performed by inputting observation data used for estimation into the geographic information system. can do. Therefore, the learning time can be significantly reduced.
  • the learned region is first included.
  • the similar region b is detected for the region to be detected, and further, the coast of Kashiwazaki, which is only off Fukuoka, will be detected as the similar region c if the detection conditions are met.
  • the sea surface temperature in the similar area can be determined. Determine the sea surface temperature, and complete the sea surface temperature distribution map for the image and similar area part. It is desirable that all of these series of operations be performed by automation after receiving a detection condition from the user. According to this configuration, After specifying the detection conditions, the user can acquire the sea surface temperature for the area that can be obtained without doing anything.
  • the sea surface temperature can be obtained for the required part.
  • the sea surface temperature may be obtained for each of the overlapping portions of one weight-the Euler network and the other weights-the Euler network.
  • the distribution map of surface temperature can be made into a smooth image. And from the distribution map of the sea surface temperature, it is also possible to obtain the value of the surface temperature in reverse.
  • the detection condition of the similar area detection means is relaxed, the error will be large and the accuracy will be low. If the detection condition is strict, the error will be small and the accuracy will be high. That is, there is a trade-off between the detection condition and the accuracy.
  • the detection conditions described above in the similar area detection means are similar to the search conditions specified in the attribute search or the spatial search, and are known techniques in the geographic information system, Although a detailed description is omitted, various designations can be made for the user to make as will be apparent to those skilled in the art. Also, image processing with a filter matrix and a weighted average filter matrix is a well-known technique, and although detailed description is omitted, various filtering processes can be applied as will be apparent to those skilled in the art.
  • the detection condition is the same area or not, the detection condition is limited to the use only in Japan, and the distance from the land is the same, and the detection condition is that the currents are similar. If so, similar region detection can be extended to the whole world.
  • the similar area detection is performed on the already learned area
  • the similar area detection can also be performed on the non-learned area.
  • the estimation system can be efficiently constructed by performing learning V in order.
  • the similar area is detected by the similar area detection means, but when learning is performed, when a certain position or area is designated, the position or area to be touched and the position already learned are already detected. Or, it is possible to judge whether the area and the force match the detection condition, and it is possible to apply the weight of the already learned position or area only to the position or area needed by the user. An estimate of the desired location or area without learning can be performed.
  • similar area detection can also be performed in the background when the CPU usage rate is low.
  • the target area for similar area detection is wide, the amount of processing for detection becomes excessive, so that the similar area can be detected smoothly by appropriately performing the process in the background.
  • the SST case is specifically described as the detection condition, but regions having the same change in time series can often be regarded as similar regions. Even in the case of SST, if the observation data or teacher data to be input data has the same change over time, it can be detected as a similar area.
  • a text box or a pull-down menu can be configured as a window in which a text box or a pull-down menu appears by designating an attribute of data, placing a check box next to each attribute name, and activating the check box.
  • Dialog Parts may be used.) 0 ) If so, what kind of data is recorded in the database 10 at the user or the position or area to be analyzed of the beginner who has started to use this system. Even a user who does not know the presence can easily set the detection conditions and can perform similar area detection smoothly.
  • the user sets the detection conditions, the user's excellent insight is often required. Therefore, it is necessary to make sure that the detection conditions set by the user are appropriate and whether they are correct as similar areas.
  • this confirmation method when the similar area is a plurality of closed areas, the user is asked for a teacher set at a certain position on each similar area, and inference is made using the input data of the teacher set, If the difference between the output data and the teacher data of the teacher set is within a predetermined threshold, it can be regarded as a similar area. Conversely, if it is outside the threshold, it is not regarded as a similar area, and the user is informed that the current detection condition is not appropriate.
  • the similar area is one large closed area, for example, the similar area is applied to the grid to obtain the grid point or the grid center as a sample point, and a teacher set of such sample points is obtained from the user and input. It can be confirmed in the same manner as above based on the teacher set. An appropriate similar area can be detected by confirming with such small sample points and judging whether it is a similar area or not.
  • the geographic information system is capable of displaying superimposed maps with topographic maps, thematic maps, etc. associated with a plurality of position information, and has the feature that spatial search, quantitative analysis and simulation are possible. .
  • this can be regarded as a hierarchical-eural network. This is called neural network geographic information system (NN-GIS). Due to this, input necessary for estimating the sea surface temperature of AVHRR band 4, 5 etc.
  • NN 1 Set AVHRR data in the input layer of GIS and display it.
  • Set V ⁇ truth data obtained in the relevant sea area to the output layer.
  • the data and output results for May 20, 2002 are as shown in Figures 13-15.
  • the data for May 20 takes the image of channel 4 (Fig. 13) and channel 5 (Fig. 14), and 16 * 16 pixels from (289, 126) off Ogura of MCSST (Fig. 15) as the correct value. ,Less than Calculated based on the teacher set (Table 2).
  • the squared error of its output is shown in ( Figure 16) [Table 2]
  • the error slightly decreases for a while and then rises rapidly to 0.1 and then gradually rises up to 0.015 at the maximum. Do. After that, it decreases smoothly to 400,000 times and converges sufficiently.
  • Teacher data in learning can be measured on site by installing a satellite data receiver on a fishing boat. Based on the acquired teacher data and satellite observation data, it is possible to easily obtain the estimation formula that is optimal for the relevant area and the relevant period.
  • Geographic information such as climatic conditions and geographical conditions for each sea area can be added to the input data, and learning data of the eural network can be efficiently used for each similar sea area. Even in areas where observation data are lacking, it is possible to simulate estimated values using observation data from other areas.

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Abstract

Le problème posé dans cette invention concerne un système d’informations géographiques possédant des données d’observation dotées d’informations géographiques pour une estimation SST et un paramètre comme donnée spatiale et pouvant exécuter l’estimation SST sur une large plage tout en réduisant le volume de calculs et les erreurs. La solution proposée par la présente invention pour résoudre ce problème consiste à attribuer au système d’informations géographiques un ensemble de règles et de le lui faire intégrer afin de constituer un réseau neutre, puis de lui faire effectuer une estimation avec des données entrées. Ainsi, le système d’informations géographiques est capable de faire une estimation ultérieure, en entrant l’ensemble de règles et en le lui faisant intégrer, et non pas par le biais d’une application de l’utilisateur d’un algorithme de type estimation de davantage de données spatiales, de telle sorte que le système puisse acquérir facilement un résultat analytique.
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CN102955878A (zh) * 2012-09-05 2013-03-06 环境保护部卫星环境应用中心 基于meris全分辨率影像数据的内陆水体光学分类方法
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