JP2008209417A - Image analysis apparatus, its method, and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an image analysis apparatus, its method, and a program which can accuratel detect the occurrence of earthquakes using the image data of a meteorological satellite. <P>SOLUTION: The meteorological image analysis apparatus 10 which analyzes the meteorological image data of the meteorological satellite includes a processing part 17 which puts the acquired meteorological image data on topographic data, and which deletes lower meteorological image data in lightness, by defining two or more lightness values for the meteorological image data, than the set values, and performs scanning, and which can exhibit existence or non-existence of earthquakes using lightness differences. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to an image analysis apparatus capable of detecting the occurrence of an earthquake, a method thereof, and a program.

Recently, many gusts of typhoons and tornadoes due to abnormal weather have occurred. When a tornado occurs, a special weather image is confirmed depending on the scale.
The meteorological satellite image is used by superimposing it on the topographical data. Where the clouds are located, the cloud flow and the typhoon Presence, pressure arrangement, etc. can be confirmed.

  Such meteorological satellite image data is updated every predetermined time (currently every 30 minutes) and used for weather forecasts, typhoon course predictions, and the like.

Meteorological satellite images include visible images and infrared images. The infrared image is different from the visible image because the altitude of the cloud top is estimated by temperature observation. However, nighttime observation is also possible.
In a special weather image that lasts for a certain period of time in an area, there is a noticeable difference in brightness in that area. Here, such a special image is referred to as an abnormal weather image.
At 9:41 on March 25, 2007, an earthquake of magnitude 6.9 occurred off the coast of Noto Peninsula. Similar abnormal weather images were confirmed two hours before and after that time in the area.

Geographical Survey Institute: Digital map 250m mesh (elevation), July 1997 Tomohiko Sugimoto: Kashmir 3D, Jitsugyo no Nihon Sha, Dec, 2003. 3D software overlooking the world, http: // earth. Google. co. jp Active Fault Study Group, New Edition Active Faults in Japan-Distribution Map and Materials, University of Tokyo Press, 4.1995

  By the way, as mentioned above, weather satellite images can confirm the position of clouds corresponding to topographic data, the flow of clouds accompanying the low pressure of large forces and the presence of typhoon eyes, but such as earthquakes The occurrence is extremely difficult to confirm or predict from the characteristics of the image.

For the detection of earthquakes, it is considered that there is a possibility that clues for elucidation can be obtained by displaying the images of meteorological satellites in three dimensions.
However, for example, a visible image of a meteorological satellite including a large typhoon has a high brightness and a small brightness difference when there are thick clouds covering the whole of Kyushu and Shikoku. is there.

  Although it is possible to use infrared images with a clearer three-dimensional display, weather data has less pixels and is coarser than terrain data, so it is difficult to use it for detecting abnormal weather conditions at present. .

  An object of the present invention is to provide an image analysis apparatus, a method thereof, and a program capable of accurately detecting the occurrence of an abnormal state such as an earthquake using image data of a meteorological satellite.

  A first aspect of the present invention is an image analysis apparatus for analyzing weather image data from a meteorological satellite, wherein the acquired weather image data is placed on topographic image data, and a plurality of brightness values are set for the weather image data. Therefore, a weather image data having a lightness lower than the set value is deleted, a scan process is performed, and a processing unit capable of expressing the presence or absence of an earthquake using the lightness difference is provided.

  Preferably, the processing unit performs a process of increasing the number of data by linear interpolation between the acquired data of the weather image data.

  Preferably, the processing unit creates a Fourier series approximation curve by fast Fourier transform, and increases the amount of the weather image data.

  Preferably, the processing unit deletes at least a meridian, a latitude line, and a terrain boundary line having high brightness in the weather image data, and replaces with a cloud around the line or adjacent brightness to correct the image to 3 Make it appear dimensionally.

  Preferably, the processing unit deletes data indicating a sea area and a lake from the terrain image data and displays the data three-dimensionally.

  Preferably, the processing unit is capable of displaying weather image data on topographic image data and displaying an image at a predetermined time when walking through the image.

  According to a second aspect of the present invention, there is provided an image analysis method for analyzing meteorological image data from a meteorological satellite, the acquired meteorological image data is placed on the topographic image data, and a plurality of brightness values are set in the meteorological image data. Thus, the weather image data having a lightness lower than the set value is deleted and the scan process is performed, and the presence or absence of an earthquake is expressed using the lightness difference.

  According to a third aspect of the present invention, there is provided a program for causing a computer to execute a process of analyzing weather image data by a weather satellite, placing the weather image data acquired in the terrain image data, and adding the weather image data to a plurality of brightness values. Is a program that deletes weather image data having a lightness lower than the set value to perform scan processing, and causes the computer to execute processing for displaying the presence / absence of a weather abnormality using the lightness difference.

  According to the present invention, it is possible to accurately detect the occurrence of an earthquake by using the brightness difference between image data of weather satellites.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings.

  FIG. 1 is a block diagram illustrating a configuration example of a weather image analysis apparatus (weather abnormality detection apparatus) according to an embodiment of the present invention.

  As shown in FIG. 1, the image analysis device (abnormality detection device) 10 includes a terrain data storage unit 11, a weather image data providing unit 12, a weather image data acquisition unit 13, a storage unit 14, an operation unit 15, a display unit 16, And a processing unit 17 as a main component.

The terrain data storage unit 11 stores, for example, terrain data of 1 km mesh among the terrain data disclosed by the Geospatial Information Authority of Japan as various numerical maps from 5 m to 1 km mesh (see, for example, Non-Patent Document 1).
The terrain data storage unit 11 is formed as a storage medium such as a CDROM, for example, and is set in an optical recording / reproducing apparatus (not shown) that can be accessed by the processing unit 17. The storage unit 17 reads out the stored data.
The numerical data of the read terrain data is formed so that the sea area at an altitude of 0 m is displayed as -9999 and the lake as 00000 in addition to the terrain altitude.

The meteorological image data providing unit 12 is formed as a database that stores meteorological image data captured through a meteorological satellite, and is configured to be acquired via the meteorological image data acquiring unit 13.
The weather image data providing unit 12 is picked up as an infrared image or a visible image through a weather satellite, updated every 30 minutes, and data for a predetermined period is accumulated.

In response to the weather image data acquisition instruction from the processing unit 17, the weather image data acquisition unit 13 performs negotiation (data exchange procedure) by sending / receiving a data request or the like to the weather image data providing unit 12 and the weather image data. To get.
The weather image data acquired by the weather image data acquisition unit 13 is stored in the storage unit 14.

The storage unit 14 stores a main program of the processing unit 17, a weather image analysis program, a calculation program, and the like.
Further, the storage unit 14 stores weather image data acquired for image analysis, topographic data, data being analyzed, or the like temporarily or as saved data.
The storage unit 14 is accessed by the processing unit 17, and data is read or written.

  The operation unit 15 includes a keyboard, a mouse, and the like. The operation unit 15 is used to input a command or setting value for executing a desired program and to instruct the processing unit 17 in the processing unit 17.

The display unit 16 is configured by various display devices such as a CRT (Cathode Ray Tube) and a liquid crystal display panel (LDC), for example, and the weather image data acquired for the acquired image analysis is displayed on the monitor unit of the display unit 16. , Terrain data, analysis processing data, analysis results are displayed.
Further, a GUI (Graphical User Interface) screen for instructing processing to the processing unit 17 is displayed on the monitor unit of the display unit 16.

The processing unit 17 controls each unit of the entire analysis apparatus 10.
In response to an operation input from the operation unit 15, the processing unit 17 acquires terrain data by the terrain data storage unit 11, acquires weather image data through the weather image data acquisition unit 13, and stores acquired data for weather image analysis. Storage processing and reading processing to the unit 14 are performed.
Further, the processing unit 17 performs a modification process of the acquired data, a synthesis process, a scan process using a plurality of brightness values of the weather image data as threshold values, and the like as described in detail below. The analysis process is performed, and the weather image data acquired for the acquired image analysis, the topographic data, the data of the analysis process, the three-dimensional display control on the display unit 16 of the analysis result, and the like are performed.

  Hereinafter, the weather image data analysis processing according to the present embodiment will be described with reference to the drawings, focusing on the processing of the processing unit 17.

  First, an outline of the processing of the processing unit 17 will be described.

<Outline of processing>
Recently, many attempts have been made to display three-dimensional terrain data (see Non-Patent Document 2 or Non-Patent Document 3).
The topographic data is disclosed by the Geographical Survey Institute as various numerical maps from 5 m to 1 km mesh (Non-Patent Document 1 described above).
In the present embodiment, for example, the topography is three-dimensionally displayed using a 1 km mesh. In addition to the topographic altitude, this numerical data is displayed as -9999 for the sea area at an altitude of 0 m and 00000 for the lake. First, these sea areas and lakes are deleted from the topographic data and displayed three-dimensionally. By eliminating sea areas and lakes, the terrain display becomes clearer and the image display time can be shortened.

Next, in the present embodiment, weather image data of the weather satellite Himawari is placed on the terrain data, and three-dimensionally displayed using the brightness difference. At that time, data with low brightness is deleted and displayed. This makes it possible to display a thick cloud with high brightness. The brightness of the image can take a value of 256 from 0 to 255. The closer the brightness value is to 255, the more the image is displayed. The closer the brightness value is to 0, the more black the image is displayed.
In addition, in the same area of terrain image data and weather image data, weather image data has a smaller number of pixels and is coarser than terrain image data. A Fourier series approximate curve is created by Fourier transform, and the number of data is increased.

More specific processing of meteorological image data analysis processing in the processing unit 17 will be described in order.
Here, weather image data on Kyushu topography from 11:00 to 23:00 on the 17th is analyzed for Typhoon No. 13 in September 2006, and the fluctuations on topography are considered.

<Deleting sea area and lake in topographic data>
As described above, in the 1 km mesh numerical map, in addition to the topographic altitude, the sea area at an altitude of 0 m is displayed as -9999 and the lake as 00000. The number of matrices is 80 × 80, and the terrain obtained by connecting the units can be displayed.
Here, the unit is connected and the matrix of the terrain data of Kyushu is created. In order to display the terrain elevation data three-dimensionally, the data is DXF (Drawing Interchange File) converted and read by a three-dimensional CAD to create a terrain mesh.
At that time, the mesh below 0m above sea level in the lake and sea area is deleted.

  2A and 2B are diagrams showing an example in which the number, node number, and coordinate value are added to the mesh of the terrain data, and FIG. 2A shows the state before the mesh is deleted. (B) shows after the mesh is deleted.

As shown in FIG. 2A, the terrain data is obtained by adding the number, the node number, and the coordinate value to the mesh. Thereafter, the mesh including the node at the altitude 0, for example, the meshes of numbers 6, 7, 10, and 11 in FIG.
As shown in FIG. 2B, except for the deleted mesh number, the meshes are rearranged into serial numbers, and their coordinate values are changed. Furthermore, rearrange the node numbers into serial numbers.
Thereby, the node numbers are added in ascending order from 1, and DXF conversion becomes possible.

3 (A) and 3 (B) are diagrams showing topographic images of Kyushu before and after the sea area and lake are deleted, and FIG. 3 (A) is a diagram showing an image before the sea area and lake are deleted. FIG. 3B is a diagram illustrating an image after the sea area and the lake are deleted.
FIG. 4 is a diagram illustrating a three-dimensional display image of Kyushu after the sea area and the lake are deleted.

FIG. 3 and FIG. 4 are obtained by connecting units of matrix number 80 × 80 in 5 rows and 4 columns, and are displayed before and after the sea area and lake meshes are deleted.
In the case of performing color display, for example, it is possible to display by coloring in the order of blue to red in order from low to high altitude.

<3D display of weather image data>
5A and 5B are diagrams showing infrared images before and after deletion of meridians and latitudes, and FIG. 5A is a diagram showing images before deletion of meridians and latitudes. B) is a diagram showing an image after deleting meridians and latitudes.
In addition to cloud images, meteorological satellite images include meridians, latitudes, topographical boundaries, and the like with high brightness, so it is necessary to delete these lines and display only the brightness of the clouds.
Therefore, as shown in FIGS. 5A and 5B, the image is corrected to an image in which unnecessary data is deleted by replacing with a cloud around these lines or adjacent brightness.
In addition, meteorological satellite images include visible images and infrared images. The infrared image is different from the visible image because the altitude of the cloud top is estimated by temperature observation. However, nighttime observation is also possible.

Typhoon No. 13 in September 2006 had strong power, the lowest atmospheric pressure was 919 hPa (3 to 9 o'clock on the 16th), and the power landed in the north of Kyushu at 950 hPa (18 o'clock on the 17th) without much decline. Since the visible image covers the entire Kyushu area and there is little brightness difference, 3D display using the brightness difference is impossible.
Therefore, here, an infrared image with a clearer three-dimensional display is used. Then, as shown in FIG. 5B, the infrared image after the deletion of meridians and latitudes is used.

In addition, in the same area of the terrain and the weather image, the weather image data has a smaller number of pixels and becomes coarser than the terrain data.
Therefore, in this embodiment, linear interpolation is performed between data, a Fourier series approximate curve is created by fast Fourier transform, and the number of data is increased and displayed.

Next, the weather image data with low brightness is deleted and displayed in the same manner as the deletion of the sea area and lake in the topographic data.
This makes it possible to display a thick cloud with high brightness.
FIG. 6 is a three-dimensional display example of infrared images before and after linear interpolation.

6A and 6B are diagrams showing infrared image display before and after linear interpolation. FIG. 6A shows an infrared image before linear interpolation (76 rows and 62 columns) with a small number of pixels in the image. FIG. 6B is a diagram showing an infrared image after linear interpolation (3 times, 60%). 6A and 6B show images displayed with the brightness value 230 or less deleted.
As shown in FIGS. 6A and 6B, when the image before linear interpolation with a small number of pixels is linearly interpolated, the image after linear interpolation is three times as many pixels (number of terms in the series 60%). It is a fine and smooth curved surface.

  In this embodiment, meteorological image data is linearly interpolated, for example, based on a discrete image data group sampled at equal intervals or a discrete image data group sampled at unequal intervals. Data for regression that is a predetermined multiple of the number of data in the data group is generated, and the regression data is regressed to a Fourier series whose number of terms is smaller than the number of data in the image data group to approximate the original image data group. As a result, each data of the data group obtained by the approximation changes smoothly and the accuracy of the approximation is improved.

That is, in the present embodiment, approximation is performed by regressing an image data group to a Fourier series.
A technique for regressing a discrete data group to a Fourier series will be briefly described below.

When the Fourier series is used, a curve of a discrete data group to be reproduced by a regression curve can be expressed by a single expression using a trigonometric function.
If a function x (t) in which N pieces of data are sampled at equal intervals is represented by a Fourier series, A ₀ , A ₁ , A ₂ ,..., A _k and B ₀ , B ₁ , B ₂ ,. _{When k} is a constant that can be appropriately determined, x (t) = A ₀ + A ₁ cost + A ₂ cos 2t +... + A _k coskt +... + B ₀ + B ₁ sint + B ₂ sin 2t + ... + B _k sinkt +.
The above formulas can be summarized as the following formula (1).

In equation (1), Δt represents the sampling interval, and NΔt represents the duration of the function x (t).
Equation (1) is an infinite series summing from 0 to infinity with respect to k, but when censored up to k = N / 2, it becomes a finite trigonometric series represented by the following equation (2).

When the function represented by the above equation (2) is converted into a function that passes N sample values x _m (m = 0, 1, 2,..., N−1) sampled at the sampling interval Δt, The sample value x _m can be expressed as the following equation (3).

The coefficients A _k and B _k in the above equation (3) can be expressed by the following equations (4) and (5), respectively.

Function represented by the above formula (3) is all accurately pass through the sample values x _m. Therefore, if the above equation (3) is used, the approximate curve can be obtained by regressing the image data group to the Fourier series.

However, when the function of Expression (3) is used as it is and the regression is performed so as to pass through the entire image data group, the approximate curve obtained by the regression to the Fourier series is as fine as the graph GDC1 shown in FIG. It becomes a curve that repeats increasing and decreasing.
In order to prevent the data value after Fourier series approximation from increasing or decreasing finely and to obtain a smooth approximate curve, in this embodiment, the number of terms of the function of equation (3), that is, the coefficient A _k. , B _k is set to a predetermined number smaller than the number of data of the image data group obtained in step ST1, and the image data group is regressed to the Fourier series.

The number of terms in the Fourier series is preferably about several tens of percent of the number of data in the image data group for specifying contour lines and the like.
If the number of data in the image data group is too small, the number of terms p decreases so that sufficient approximation accuracy cannot be obtained. Therefore, although depending on the required approximation accuracy, in order to increase the number of terms p and ensure a certain degree of approximation accuracy, the number of data of the original image data group is also required to some extent. The approximation accuracy improves as the number of data in the image data group increases.

As the number of terms p decreases, the calculation time of the function of Equation (3) becomes shorter and the obtained approximate curve tends to be smoother.
However, when the number of terms p decreases, the error between the approximate value increases in the rear part of the image data group. Therefore, in order to increase the accuracy of approximation, in this embodiment, an additional data group is added to the image data group, and the number of data to be returned to the Fourier series is increased to a predetermined multiple of the image data group.

The processing unit 17 generates an additional data group to be added to the obtained image data group in order to increase the number of data to be returned to the Fourier series.
Since the Fourier series is a periodic function, in order to improve the accuracy of approximation using this property, the additional data group is composed of image data centered on the last data of the image data group in terms of its size. Data is generated as a data group arranged so as to be symmetric with respect to the data size of each group.
In the present embodiment, a graph GDC1 ′ obtained by connecting individual data of the additional data group by a broken line is a graph with respect to a vertical line passing through the last point of the graph GDC1 of the image data group as shown in FIG. An additional data group is generated so as to be line symmetric with respect to GDC1.
The additional data group may be generated so that the graph GDC1 ′ is point-symmetric with respect to the graph GDC1 with the last point of the graph GDC1 as the center, but from the viewpoint of improving the approximation accuracy, as shown in FIG. It is preferable that data such that the graph is line symmetric be the additional data group.

As described above, by adding the additional data group arranged so as to be symmetric to the last data of the image data group to the image data group, the number of sample values in Equation (3) is not N but αN. can do. The data group having αN pieces is called a regression data group.
Here, the coefficient α is a natural number from the viewpoint of increasing the number of data. In particular, the coefficient α is preferably an even number from the viewpoint of improving the accuracy of approximation using the periodic nature of the Fourier series. This is because if the coefficient α is an even number (for example, 2), all the data of the original image data group is used as data having a symmetric size around the last data of the image data group.
However, from the viewpoint of increasing the number of data, the coefficient α can be a positive decimal number.
In order to set the coefficient α to 2 or more so that the number of data is more than twice the first N data, the regression data group generated based on the first image data group is considered as the first data group, and this What is necessary is just to produce | generate a new data group so that it may become symmetrical centering on the last data of a data group.

  The processing unit 17 regresses the data group for regression obtained by generating as described above to the Fourier series.

FIG. 7B shows a graph FGDC1 of an approximate curve obtained by regressing the regression data group to the Fourier series. The horizontal axis in FIG. 7B represents the distance [mm] from the reference position A on the same measurement axis MAL as in FIG. 7A, and the vertical axis represents the magnitude of luminance.
When the regression data group is regressed to the Fourier series, an approximate curve for the entire regression data group can be obtained. However, only the analysis result relating to the image data group on the measurement axis MAL is used for specifying the position of the color matching line. Since it is necessary, FIG. 7B shows only the graph FGDC1 of the approximate curve of the portion related to the original image data group.

As is clear from the graph FGDC1, the value of each data changes smoothly by reducing the number of terms p to some extent and regressing to the Fourier series.
In addition, since the regression is performed using data with an increased number including the additional data group, each data after approximation to the Fourier series changes smoothly, but the error with the original image data group becomes small, The accuracy of approximation is improved.
By using the original image data group, it is considered that the accuracy of approximation improves as the generated regression data group becomes periodic data. However, the smoothness and approximation accuracy of the obtained approximate curve change in a complex manner depending on the characteristics of changes in the image data group and the combination of parameters such as the number of terms p and the number of data in the regression data group, and are not decided unconditionally. .

  And the luminance value by the Fourier approximation curve obtained in this way was DXF-converted and displayed as a three-dimensional mesh image. Note that ArchiCAD7 (Graphic Software) or the like can be used to display a three-dimensional mesh image, but other three-dimensional display software may be used.

  Next, three-dimensional display of a topographic image and a weather image will be described.

<3D display of topographic and weather images>
In the area of Japanese terrain as the base point of weather images, four intersections of 130 degrees east longitude, 30 degrees north latitude, 140 degrees east longitude, 30 degrees north latitude, 130 degrees east longitude, 40 degrees north latitude, 140 degrees east longitude, and 40 degrees north latitude can be used. .
FIG. 8 is a diagram showing four intersections in a Japanese landform area as a base point of a weather image.
The processing unit 17 causes the terrain image and the weather image to coincide with each other with the intersection as a base point and displays both images on the display unit 16.
In the Kyushu terrain, the intersection is 130 degrees east longitude and 30 degrees north latitude. In addition, the weather data is enlarged by about 1.7 times with respect to the terrain data so that the size matches the terrain.

  A specific example in which the above embodiment is applied to actual earthquake detection will be described below.

The earthquake off Noto Peninsula Around 9:41 on March 25, 2007, an earthquake occurred in Ishikawa Prefecture. The epicenter is off the coast of Noto Peninsula (37.3 ° north latitude, 136.5 ° east longitude), the depth of the epicenter is about 11km, and the magnitude of the earthquake (magnitude) is estimated to be 6.9. The maximum seismic intensity is 6+ in Tazuruhama-cho, Nanao-shi. Information on seismic intensity and earthquake information announced by the Japan Meteorological Agency are shown in FIGS. Seismic intensity 1 and 2 are distributed over a wide area, starting from the offshore Ibaraki prefecture.

  The active faults and earthquake distribution around Ishikawa Prefecture are shown in FIG. 11 (Non-patent Document 4). Active faults are distributed near Ishikawa and Niigata prefectures.

  For weather images around 9:00, we will examine in detail the images near the Noto Peninsula. The weather image at each time is shown in FIG. In the vicinity of the seismic intensity distribution in FIG. 9, the brightness changes in the image.

  FIG. 13 is an enlarged image of the vicinity of Ishikawa Prefecture, and the right is an image after deleting meridians and the like. Two base points are indicated by ← as the corresponding position between the weather image and the topography.

14 to 21 are images in which weather images at various times are placed on topography near Ishikawa Prefecture. In the plane (top) and the cross section, ↑ indicates the direction of the line of sight.
14 and 15 show images at 7:30, and the plane is an image with the brightness value 190 or less deleted. The lowest brightness value of the section of the deleted area A is 159, the highest is 181 and the brightness difference is 22. Here, the highest value was averaged by finding the highest value around the lowest value. The values are east 171, west 191, south 166 and north 197. The cross section is displayed in the north-south direction.

  Similarly, FIGS. 16 and 17 are images obtained by deleting the lightness value of 195 or less on the display at 8:00. There are four areas with low brightness, A, B, C, and D. The east-west section (1) and north-south sections (2), (3) show the area. The lowest brightness value of the cross section of area B is 149, the highest is 197, and the brightness difference is 48.

  FIG. 18 shows an image from which 8:30 is displayed and the plane is deleted with a lightness value of 190 or less. The lowest brightness value of the cross section in the area F is 140, the highest is 199, and the brightness difference is 59.

  FIG. 19 shows an image obtained by deleting the lightness value of 190 or less on the display at 9:00. The lowest brightness value of the cross section in the area A is 132, the highest is 216, and the brightness difference is 84. At this time, the brightness difference in area A is maximized. A sharp change in the brightness value occurs in this region in the range of about 139 km on the east-west section. Around 41 minutes after this time, an earthquake occurred off the coast of area A.

  FIG. 20 shows an image obtained by deleting the lightness value of 180 or less on the display at 10:00. The lowest brightness value of the cross section of area B is 138, the highest is 203, and the brightness difference is 65. The value has decreased from the maximum value at 9:00.

  FIG. 21 shows the A area in a 3D display at 9:00. In the figure, the line of sight is from southwest to northeast. Below is the line of sight from southeast to northwest. Region A is on the coast of the terrain.

  22 to 27 are images in which brightness values of 133 to 180 or less are deleted on a plane display at 9:00. A low-light area extends from the inland area of the Noto Peninsula to the epicenter.

As is clear from the above analysis, in the earthquake off Noto Peninsula, it was found that the intensity difference near the epicenter was large, and the seismic intensity in the wide area was large.
Thus, it was found that the position of the earthquake can be specified (detected) from the brightness difference of the weather image data.

Earthquake in southern Ibaraki Prefecture An earthquake occurred in Ibaraki Prefecture around 13:34 on June 4, 2007. The epicenter is the southern part (36.1 ° north latitude, 139.9 ° east longitude), the depth of the epicenter is about 50km, and the magnitude of the earthquake is estimated to be 4.4. The maximum seismic intensity is 3 in Taisho-cho, Ashikaga City. FIG. 28 shows information on seismic intensity at various locations announced by the Japan Meteorological Agency, and earthquake information. Seismic intensity 1 and 2 are distributed over a wide area, starting from the offshore Ibaraki prefecture.

  The active faults and earthquake distribution around Ibaraki Prefecture are shown in FIG. Active faults are distributed near the southern part of Ibaraki Prefecture.

  For weather images around 13:00, we will examine in detail the image near the southern part of Ibaraki Prefecture. The weather image at each time is shown in FIG. In the vicinity of the seismic intensity distribution in FIG. 28, the brightness changes in the image.

  FIG. 31 is an enlarged image of the vicinity of Ibaraki Prefecture, and the right is an image after deleting meridians and the like. Two base points are indicated by ← as the corresponding position between the weather image and the topography.

32 to 38 are images in which weather images at various times are placed on topography near Ibaraki Prefecture. In the plane (top) and the cross section, ↑ indicates the direction of the line of sight.
FIG. 32 shows an image in which the display is deleted at 11:30, and the plane is deleted with a brightness value of 51 or less. The lowest brightness value of the section of the deleted area A is 26, the highest is 61, and the brightness difference is 35. Here, the highest value was averaged by finding the highest value around the lowest value. The values are east 56, west 54, south 49, and north 85. The cross section is shown in two directions, east-west and north-south.

  Similarly, FIG. 33 is an image obtained by deleting the lightness value of 52 or less on the display at 12:00. There are five areas with low brightness, A, B, C, D, and E. The east-west section (1) and north-south sections (2), (3) show the area. The lowest brightness value of the cross section of area B is 24, the highest is 62, and the brightness difference is 38.

  FIG. 34 shows an image from which 12:30 is displayed and the plane is deleted with a brightness value of 53 or less. The lowest brightness value of the cross section of area A is 24, the highest is 62, and the brightness difference is 38.

  FIG. 35 shows an image obtained by deleting a lightness value of 50 or less on the display at 13:00. The lowest brightness value of the cross section of area A is 23, the highest is 63, and the brightness difference is 40.

  FIG. 36 shows an image in which the display has a brightness value of 56 or less on the display at 13:30. The lowest brightness value of the cross section of area A is 22, the highest is 66, and the brightness difference is 44. At this time, the brightness difference in area A is maximized. Abrupt changes in brightness values have occurred in this region, ranging from about 114 km on the east-west section and about 113 km on the north-south section. Around 4 minutes after this time, an earthquake occurred in the eastern part of area A.

  FIG. 37 shows an image obtained by deleting the lightness value of 50 or less on the display at 14:00. The lowest brightness value of the cross section of area A is 25, the highest is 67, and the brightness difference is 42. Its value has decreased from the maximum at 13:30.

  FIG. 38 displays area A in a 3D display at 13:30. The line of sight is from the northeast to the southwest. Below is the line of sight from the southeast to the northwest. Area A is on the plain of the topography.

  39 to 44 are images obtained by deleting lightness values of 22 to 51 or less in a 13:30 plane display. A low-light area extends from the inland area of southern Ibaraki Prefecture to the eastern part of the epicenter.

  As is clear from the above, it is possible to analyze the brightness of an area of magnitude 4 and seismic intensity 3 in the southern Ibaraki earthquake.

Hokkaido Nemuro Branch Northern Earthquake Around 13:12 on July 01, 2007, an earthquake occurred in eastern Hokkaido. The epicenter is the northern part of Nemuro branch (43.5 ° north latitude, 144.9 ° east longitude), the depth of the epicenter is about 130km, and the magnitude of the earthquake (magnitude) is estimated to be 5.6. The maximum seismic intensity is 4 in Yukimachi, Kushiro City.
Information on seismic intensity and earthquake information announced by the Japan Meteorological Agency are shown in FIGS. Seismic intensity 1 and 2 are distributed over a wide area starting from the northern part of the Nemuro branch office.

  Fig. 47 shows active faults and earthquake distribution around Hokkaido. Active faults are distributed near the Ishikari Branch, Tokachi Branch and Nemuro Branch.

  For the weather image of 2 hours before and after 13:00, examine the Hokkaido image in detail. The weather image at each time is shown in FIG. In the vicinity of the seismic intensity distribution in FIG. 46, a change in brightness occurs in the image.

  FIG. 49 is an enlarged image of the vicinity of Hokkaido, and the right is an image after deleting meridians and the like. Two base points are indicated by ← as the corresponding position between the weather image and the topography. In addition, an average image every 30 minutes from 11:30 to 14:30 is shown.

50 to 59 are images in which weather images at various times are placed on the topography of Hokkaido. In the plane (top) and the cross section, ↑ indicates the direction of the line of sight.
FIG. 50 is an image in which the display is deleted at 11:30 with the brightness value of 48 or less. The minimum brightness value of the section of the deleted area A is 50, the maximum is 74, and the brightness difference is 24. Here, the highest value was averaged by finding the highest value around the lowest value in the north and south. The values are 70 north and 77 south. Similarly, each value was obtained for the B region and the C region. Area A is the Ishikari branch office, area B is the Tokachi branch office, and area C is the Nemuro branch office of the epicenter.

  Similarly, FIG. 51 is an image obtained by deleting the brightness value of 48 or less on the display at 12:00. The brightness difference in region B is 37.

  FIG. 52 shows an image from which 12:30 is displayed and the plane is deleted with a brightness value of 47 or less. At this time, the brightness difference in region B reaches a maximum value of 39. A sudden change in brightness value occurs in this region in the range of about 57 km.

  FIG. 53 shows an image obtained by deleting the lightness value of 46 or less on the display at 13:00. The brightness difference in region B is 38. Around 13:12, an earthquake occurred in area C.

  In FIG. 54, cross sections were obtained in two directions on the plane at 13:00. The brightness difference of the cross section 1 is larger than that of the cross section 2.

  FIG. 55 shows an image in which the display has a brightness value of 45 or less on the display at 13:30. Cross sections were obtained in two directions, east-west and north-south on the plane. In the north-south section, the brightness difference in the left part corresponding to the northern part of the Nemuro branch is large. The brightness difference in region B is 33.

  FIG. 56 shows an image obtained by deleting the lightness value of 46 or less on the display at 14:00. The brightness difference in area B is 35.

  FIG. 57 shows an image in which the display has a brightness value of 48 or less on the display at 14:30. The brightness difference in region B is 36.

  FIG. 58 averaged the lightness values from 11:30 to 14:30. The plane is an image from which the brightness value of 50 or less is deleted. The brightness difference in region B is 36. The C region of the epicenter is 29, which is smaller than the brightness difference of the B region.

  FIG. 59 shows an image obtained by deleting a lightness value of 61 or less on a plane display at 13:00. The Ishikari branch office, Tokachi branch office, and the Nemuro branch office near the epicenter have been deleted.

  FIG. 60 shows the A, B, and C areas in a 13:00 3D display. In the figure, the line of sight is from southwest to northeast. Below is the line of sight from southeast to northwest. Each region is on a terrain plain.

  61 to 67 are images obtained by deleting lightness values of 46 to 59 or less on a plane display at 13:00. Areas with low lightness started from the vicinity of the Ishikari branch office and spread to Tokachi branch office and Nemuro branch office.

  As described above, in the northern Hokkaido Nemuro branch office, the lightness is low near the active fault distribution, and the lightness difference is large.

Niigata Chuetsu-oki Earthquake An earthquake occurred in Niigata Prefecture around 10:13 on July 16, 2007. The epicenter is off Jochuetsu (37.5 ° north latitude, 138.6 ° east longitude, 60km southwest of Niigata), the depth of the epicenter is about 10km, and the magnitude of the earthquake (magnitude) is estimated to be 6.8. The maximum seismic intensity is 6+ in Yodozuna-cho Yodogawa. 68 and 69 show information on seismic intensity and earthquake information announced by the Japan Meteorological Agency. Seismic intensity 1, 2 and 3 are distributed over a wide area, starting from the offshore of Chuchu-Etsu in the epicenter.

  Fig. 70 shows the active faults and earthquake distribution around Niigata Prefecture. Active faults are distributed near Niigata and Ishikawa prefectures.

  For weather images around 10:00, we will examine in detail the images near Niigata Prefecture. A weather image at each time is shown in FIG. In the vicinity of the seismic intensity distribution of FIGS.

  FIG. 72 is an enlarged image of the vicinity of Niigata prefecture, and the right is an image after deleting meridians and the like. Two base points are indicated by ← as the corresponding position between the weather image and the topography.

73 to 77 are images in which weather images at various times are placed on topography near Niigata Prefecture. In the plane (top) and the cross section, ↑ indicates the direction of the line of sight.
FIG. 73 shows an image in which the display is at 8:30, and the plane is deleted with a brightness value of 80 or less. The lowest brightness value of the section of the deleted area A is 71, the highest is 85, and the brightness difference is 14. Here, the highest value was averaged by finding the highest value around the lowest value. The values are North 89, South 73, West 89, and East 88. The cross section is displayed in two directions, east-west and north-south.

  Similarly, FIG. 74 is an image in which the display at 9:00 is deleted and the plane is deleted from the brightness value 81 or less. There are three areas with low brightness, A, B and C. The north-south section 1 and east-west sections 2, 3 show the area. The lowest brightness value of the cross section of area A is 65, the highest is 83, and the brightness difference is 18.

  FIG. 75 shows an image in which the display is deleted at 10:00 and the plane is deleted from the brightness value 80 or less. The lowest brightness value of the cross section of area A is 61, the highest is 93, and the brightness difference is 32. At this time, the brightness difference in area A is maximized. There is a sudden change in the brightness value in this region in the range of about 73 km on the north-south section. Around 13 minutes after this time, an earthquake occurred on the coast of area B.

  FIG. 76 shows an image from which the brightness value of 80 or less is deleted on the 10:30 display. The lowest brightness value of the cross section of area C is 60, the highest is 81, and the brightness difference is 21.

  FIG. 77 shows an image obtained by deleting the lightness value of 80 or less on the display at 11:00. The lowest brightness value of the cross section of area B is 63, the highest is 94, and the brightness difference is 31.

  In FIG. 78, the A area is displayed in 3D display at 10:00. The figure shows the line of sight from the east to the west. The figure below shows the line of sight from northwest to southeast. Area A is on the plain of the topography.

  79 to 85 are images in which brightness values of 62 to 80 or less are deleted on a plane display at 10:00. A low-light area extends from the inland area of Niigata Prefecture to the northwest coast of the epicenter.

Aftershocks in Niigata Prefecture Aftershocks occurred around 15:37 on July 16, 2007 in Niigata Prefecture. The epicenter is off Jochuetsu (37.5 ° north latitude, 138.7 ° east longitude, 60km southwest of Niigata), the depth of the epicenter is about 10km, and the magnitude of the earthquake (magnitude) is estimated to be 5.6. The maximum seismic intensity is less than 6 in Kojimaya, Nagaoka City. 86 and 87 show information on seismic intensity at various locations announced by the Japan Meteorological Agency and earthquake information. Seismic intensity 1, 2 and 3 are distributed over a wide area, starting from the offshore of Chuchu-Etsu in the epicenter.

  For weather images around 16:00, we will examine in detail the images near Niigata Prefecture. A weather image at each time is shown in FIG. In the vicinity of the seismic intensity distribution in FIGS. 86 and 87, the brightness changes in the image.

  FIG. 89 is an enlarged image of the vicinity of Niigata Prefecture, and the right is an image after deleting meridians and the like. Two base points are indicated by ← as the corresponding position between the weather image and the topography.

90 to 92 are images in which weather images at various times are placed on topography near Niigata Prefecture. In the plane (top) and the cross section, ↑ indicates the direction of the line of sight.
FIG. 90 shows an image obtained by deleting the lightness value of 126 or less on the display at 15:00. The lowest brightness value of the section of the deleted area B is 73, the highest is 144, and the brightness difference is 71. At this time, the brightness difference in area A is maximized. The cross section is displayed in two directions, east-west and north-south. There is a sudden change in the brightness value in this area in the range of about 82 km on the north-south section. Around 37 minutes after this time, an earthquake occurred on the coast of region C.

  Similarly, FIG. 91 shows an image in which the display is deleted at 16:00, and the plane is deleted with a brightness value of 128 or less. The lowest brightness value of the cross section of area B is 101, the highest is 153, and the brightness difference is 52.

  FIG. 92 is an image in which the display is deleted at 16:30, and the plane is deleted with a brightness value of 135 or less. The lowest brightness value of the cross section of area B is 92, the highest is 149, and the brightness difference is 57.

  FIG. 93 displays the area A in 3D display at 15:00. In the figure, the line of sight is from southwest to northeast. The figure below shows the line of sight from northwest to southeast. Region B is on the terrain coast.

  FIGS. 94 to 99 are images obtained by deleting lightness values of 76 to 126 or less on a plane display at 15:00. A low-light area extends from the southwest coast of Niigata Prefecture to the northeast of the epicenter.

  As can be seen from the above, the Niigata Chuetsu-oki earthquake has large and small differences in brightness, depending on the weather conditions, even if the seismic intensity in that region is large.

As is clear from the above, meteorological image data at the time of the earthquake was displayed three-dimensionally and the epicenter was identified from the brightness difference.
That is, from a specific example, it is possible to analyze the brightness of an abnormal image about two hours before the occurrence of an earthquake from the result of an earthquake weather image analysis. Moreover, the following can be said from image analysis at each earthquake.
(1) In the earthquake off Noto Peninsula, the brightness difference near the epicenter is large, and the seismic intensity in the wide area is large.
(2) In the southern Ibaraki earthquake, it is possible to analyze the brightness of an area of magnitude 4 and seismic intensity 3.
(3) In the Hokkaido earthquake, the lightness is low near the active fault distribution, and the lightness difference is large.
(4) Niigata Chuetsu-oki earthquakes have large and small differences in brightness depending on weather conditions, even if the seismic intensity in the region is large.

Note that the method described above in detail can be formed as a program according to the above-described procedure and executed by a computer such as a CPU.
Further, such a program can be configured to be accessed by a recording medium such as a semiconductor memory, a magnetic disk, an optical disk, a floppy (registered trademark) disk, or the like, and to execute the program by a computer in which the recording medium is set.

It is a block diagram which shows the structural example of the image-analysis apparatus (meteorological abnormality detection apparatus) which concerns on embodiment of this invention. It is a figure which shows the example which added the number, the node number, and the coordinate value to the mesh of topographic data, (A) is a figure which shows before deletion of a mesh, (B) is a figure which shows after deletion of a mesh It is. It is a figure which shows the topographic image of Kyushu before and after deletion of a sea area and a lake, (A) is a figure which shows the image before deletion of a sea area and a lake, and (B) shows the image after deletion of a sea area and a lake. FIG. It is a figure which shows the three-dimensional display image of Kyushu after deletion of a sea area and a lake. FIG. 4 is a diagram showing infrared images before and after deletion of meridians and latitude lines, (A) is a diagram showing images before deletion of meridians and latitude lines, and (B) is a diagram showing images after deletion of meridians and latitude lines. is there. It is a figure which shows the infrared image display before and behind linear interpolation, Comprising: (A) is a figure which shows the infrared image before linear interpolation (76 rows 62 columns) with few pixels of an image, (B) is a figure after linear interpolation ( It is the figure which showed the infrared image of 3 times and 60%. It is a graph which shows an image data group, (A) is a graph of the data group which regresses to a Fourier series, (B) is a graph of the approximated curve obtained by regressing the data group shown to (A) to a Fourier series. is there. It is a figure which shows four intersections in the area of Japanese topography as a base point of a weather image. It is a figure which shows the information regarding the seismic intensity of each place announced by the Japan Meteorological Agency, and earthquake information. It is a figure which shows the information regarding the seismic intensity of each place announced by the Japan Meteorological Agency, and earthquake information. It is a figure which shows the active fault around Ishikawa Prefecture, and earthquake distribution. It is a figure which shows the weather image of each time. In the enlarged image of the vicinity of Ishikawa Prefecture, the image on the right shows the image after deleting meridians. It is a figure which shows the image which put the weather image of each time on the topography near Ishikawa Prefecture. It is a figure which shows the image which put the weather image of each time on the topography near Ishikawa Prefecture. It is a figure which shows the image which put the weather image of each time on the topography near Ishikawa Prefecture. It is a figure which shows the image which put the weather image of each time on the topography near Ishikawa Prefecture. It is a figure which shows the image which put the weather image of each time on the topography near Ishikawa Prefecture. It is a figure which shows the image which put the weather image of each time on the topography near Ishikawa Prefecture. It is a figure which shows the image which put the weather image of each time on the topography near Ishikawa Prefecture. It is a figure which shows the image which put the weather image of each time on the topography near Ishikawa Prefecture. It is a figure which shows the image which deleted the brightness value 133-180 or less by the display of the plane of 9:00. It is a figure which shows the image which deleted the brightness value 133-180 or less by the display of the plane of 9:00. It is a figure which shows the image which deleted the brightness value 133-180 or less by the display of the plane of 9:00. It is a figure which shows the image which deleted the brightness value 133-180 or less by the display of the plane of 9:00. It is a figure which shows the image which deleted the brightness value 133-180 or less by the display of the plane of 9:00. It is a figure which shows the image which deleted the brightness value 133-180 or less by the display of the plane of 9:00. It is a figure which shows the information regarding the seismic intensity of each place announced by the Japan Meteorological Agency, and earthquake information. It is a figure which shows the active fault around Ibaraki Prefecture, and earthquake distribution. It is a figure which shows the weather image of each time. It is an enlarged image of the vicinity of Ibaraki Prefecture, and the right is a diagram showing an image after deleting meridians and the like. It is a figure which shows the image which put the weather image of each time on the topography near Ibaraki Prefecture. It is a figure which shows the image which put the weather image of each time on the topography near Ibaraki Prefecture. It is a figure which shows the image which put the weather image of each time on the topography near Ibaraki Prefecture. It is a figure which shows the image which put the weather image of each time on the topography near Ibaraki Prefecture. It is a figure which shows the image which put the weather image of each time on the topography near Ibaraki Prefecture. It is a figure which shows the image which put the weather image of each time on the topography near Ibaraki Prefecture. It is the figure which displayed A area by 3D display of 13:30. It is a figure which shows the image which deleted the brightness values 22-51 or less by the display of a plane for 13:30. It is a figure which shows the image which deleted the brightness values 22-51 or less by the display of a plane for 13:30. It is a figure which shows the image which deleted the brightness values 22-51 or less by the display of a plane for 13:30. It is a figure which shows the image which deleted the brightness values 22-51 or less by the display of a plane for 13:30. It is a figure which shows the image which deleted the brightness values 22-51 or less by the display of a plane for 13:30. It is a figure which shows the image which deleted the brightness values 22-51 or less by the display of a plane for 13:30. It is a figure which shows the information regarding the seismic intensity of each place announced by the Japan Meteorological Agency, and earthquake information. It is a figure which shows the information regarding the seismic intensity of each place announced by the Japan Meteorological Agency, and earthquake information. It is a figure which shows the active fault around Hokkaido, and earthquake distribution. It is a figure which shows the weather image of each time. It is an enlarged image of the vicinity of Hokkaido, and the right is a diagram showing an image after deleting meridians and the like. It is a figure which shows the image which put the weather image of each time on the landform of Hokkaido. It is a figure which shows the image which put the weather image of each time on the landform of Hokkaido. It is a figure which shows the image which put the weather image of each time on the landform of Hokkaido. It is a figure which shows the image which put the weather image of each time on the landform of Hokkaido. It is a figure which shows the image which put the weather image of each time on the landform of Hokkaido. It is a figure which shows the image which put the weather image of each time on the landform of Hokkaido. It is a figure which shows the image which put the weather image of each time on the landform of Hokkaido. It is a figure which shows the image which put the weather image of each time on the landform of Hokkaido. It is a figure which shows the image which put the weather image of each time on the landform of Hokkaido. It is a figure which shows the image which put the weather image of each time on the landform of Hokkaido. It is a figure which displays A, B, and C area by 3D display at 13:00. It is a figure which shows the image which deleted the lightness value 46-59 or less by the display of the plane of 13:00. It is a figure which shows the image which deleted the lightness value 46-59 or less by the display of the plane of 13:00. It is a figure which shows the image which deleted the lightness value 46-59 or less by the display of the plane of 13:00. It is a figure which shows the image which deleted the lightness value 46-59 or less by the display of the plane of 13:00. It is a figure which shows the image which deleted the lightness value 46-59 or less by the display of the plane of 13:00. It is a figure which shows the image which deleted the lightness value 46-59 or less by the display of the plane of 13:00. It is a figure which shows the image which deleted the lightness value 46-59 or less by the display of the plane of 13:00. It is a figure which shows the information regarding the seismic intensity of each place announced by the Japan Meteorological Agency, and earthquake information. It is a figure which shows the information regarding the seismic intensity of each place announced by the Japan Meteorological Agency, and earthquake information. It is a figure which shows the active fault around Niigata Prefecture, and earthquake distribution. It is a figure which shows the weather image of each time. This is an enlarged image of the vicinity of Niigata Prefecture, and the right figure shows an image after deleting meridians. It is a figure which shows the image which put the weather image of each time on the topography near Niigata Prefecture. It is a figure which shows the image which put the weather image of each time on the topography near Niigata Prefecture. It is a figure which shows the image which put the weather image of each time on the topography near Niigata Prefecture. It is a figure which shows the image which put the weather image of each time on the topography near Niigata Prefecture. It is a figure which shows the image which put the weather image of each time on the topography near Niigata Prefecture. It is the figure which displays A area by 3D display of 10:00. It is a figure which shows the image which deleted the brightness value 62-80 or less by the display of the plane of 10:00. It is a figure which shows the image which deleted the brightness value 62-80 or less by the display of the plane of 10:00. It is a figure which shows the image which deleted the brightness value 62-80 or less by the display of the plane of 10:00. It is a figure which shows the image which deleted the brightness value 62-80 or less by the display of the plane of 10:00. It is a figure which shows the image which deleted the brightness value 62-80 or less by the display of the plane of 10:00. It is a figure which shows the image which deleted the brightness value 62-80 or less by the display of the plane of 10:00. It is a figure which shows the image which deleted the brightness value 62-80 or less by the display of the plane of 10:00. It is a figure which shows the information regarding the seismic intensity of each place announced by the Japan Meteorological Agency, and earthquake information. It is a figure which shows the information regarding the seismic intensity of each place announced by the Japan Meteorological Agency, and earthquake information. It is a figure which shows the weather image of each time. This is an enlarged image of the vicinity of Niigata Prefecture, and the right figure shows an image after deleting meridians. It is a figure which shows the image which put the weather image of each time on the topography near Niigata Prefecture. It is a figure which shows the image which put the weather image of each time on the topography near Niigata Prefecture. It is a figure which shows the image which put the weather image of each time on the topography near Niigata Prefecture. It is the figure which displays A area by 3D display of 15:00. It is a figure which shows the image which deleted the brightness values 76-126 or less by the display of the plane of 15:00. It is a figure which shows the image which deleted the brightness values 76-126 or less by the display of the plane of 15:00. It is a figure which shows the image which deleted the brightness values 76-126 or less by the display of the plane of 15:00. It is a figure which shows the image which deleted the brightness values 76-126 or less by the display of the plane of 15:00. It is a figure which shows the image which deleted the brightness values 76-126 or less by the display of the plane of 15:00. It is a figure which shows the image which deleted the brightness values 76-126 or less by the display of the plane of 15:00.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 10 ... Image analysis apparatus (meteorological abnormality detection apparatus), 11 ... Topographic data storage part, 12 ... Weather image data provision part, 13 ... Weather image data acquisition part, 14 ... Storage part, 15... Operation unit, 16... Display unit, 17.

Claims (14)

An image analysis device for analyzing weather image data from a meteorological satellite,
The acquired weather image data is placed in the topographic image data, and by setting multiple brightness values for the weather image data, the weather image data having a lightness lower than the set value is deleted and scanning processing is performed, and the presence or absence of an earthquake is determined. An image analysis apparatus having a processing unit capable of being expressed using a difference. The processor is
The image analysis apparatus according to claim 1, wherein a process of increasing the number of data by linearly interpolating between the acquired weather image data is performed. The processor is
The image analysis apparatus according to claim 2, wherein a Fourier series approximate curve is created by fast Fourier transform to increase the amount of the weather image data. The processor is
2. At least meridian, latitude and terrain boundary lines with high brightness in the weather image data are deleted, replaced with clouds around the line or adjacent brightness, and corrected to an image to be displayed three-dimensionally. 4. The image analysis device according to any one of 3. The processor is
The image analysis apparatus according to any one of claims 1 to 4, wherein data indicating a sea area and a lake is deleted from the topographic image data to be displayed three-dimensionally. The processor is
The image analysis apparatus according to claim 1, wherein weather image data is placed on topographic image data, and an image at a predetermined time when the image is walked through can be displayed. A weather image analysis method for analyzing weather image data from a meteorological satellite,
Put the acquired weather image data on the topographic image data,
By setting a plurality of brightness values for the weather image data, the weather image data whose brightness is lower than the set value is deleted, and scanning processing is performed.
An image analysis method that expresses the presence or absence of an earthquake using the brightness difference.   The image analysis method according to claim 7, wherein the amount of the meteorological image data is increased by linearly interpolating between the data of the meteorological image data having a smaller number of pixels than the topographic image data to create a Fourier series approximate curve by fast Fourier transform. 8. At least meridian, latitude and terrain boundary lines with high brightness in the weather image data are deleted, replaced with clouds around the line or adjacent brightness, and corrected to an image to be displayed three-dimensionally. Or the image-analysis method of 8. The image analysis method according to any one of claims 7 to 9, wherein data representing a sea area and a lake is deleted from the topographic image data and is expressed three-dimensionally. A program for causing a computer to execute processing for analyzing weather image data from a meteorological satellite,
Place the acquired weather image data on the topographic image data, set a plurality of brightness values for the weather image data, delete the weather image data whose brightness is lower than the set value, perform the scanning process, A program that causes a computer to execute processing that uses lightness differences to express. The computer executes a process of linearly interpolating between data of weather image data having a smaller number of pixels than the topographic image data, creating a Fourier series approximate curve by fast Fourier transform, and increasing the amount of the weather image data. The listed program. A computer that performs three-dimensional rendering by deleting at least meridians, latitudes, and terrain boundary lines with high brightness in the weather image data, replacing them with clouds around the lines or adjacent brightness, and correcting them to an image. The program according to claim 11 or 12, wherein the program is executed. The program as described in any one of Claim 11 to 13 which makes a computer perform the process which deletes the data which show a sea area and a lake from the said topographic image data, and expresses it three-dimensionally.

Images