Fractal terrain stereoscopic visualization image generation system and fractal terrain stereoscopic visualization image generation program

Abstract

To generate a stereoscopic image of a fractal landscape that is free of jaggies, appears to have a landscape floating thereon, and furthermore has fine portions of the landscape rendered in detail.　An invention comprising a crust map memory 110, a red solid image generation unit 120, an area-reading unit 140, a culling 50 m DEM conversion unit 160, a miniaturization processing unit 180, a movement-averaging unit 210, a multiplication synthesis unit 220, a blurring 5 m DEM smooth red solid image generation unit 250, a fractal solid image generation unit 260, a display processing unit 280, and the like, thereby making it possible to obtain a stereoscopic image of a fractal landscape exhibiting three-dimensionality, the stereoscopic image being free of jaggies, appearing to have a landscape floating thereon, and furthermore having fine portions of the landscape rendered in detail.

Description

gradations. That is, 0 degree is white, and equal to or more than 50 degrees is black. The larger the slope gradient ai, the darker the color.

[0124] Then, as illustrated in Figure 22, the inclinationemphasis color assignment process 30 of the inclination emphasizer 13 stores a difference image between the under-ground openness image data Db and the over-ground openness image data Da as a slope gradient image Dra in a memory 31. In doing so, color data based on the third gray scale 11C is assigned to a mesh with the same Z-value as the subject point (coordinates).

[0125] Next, a reddening process 32 emphasizes R by an RGB color mode function (however, sometimes 50 percent emphasis may be used). That is, the slope-gradient emphasis image Dr in which the steeper the slope, the more the red is emphasized is stored in a memory 33 (layer). The first synthesizer 14 obtains a synthesized image Dh synthesized by multiplying the over-ground openness layer Dp and the under-ground openness layer Dq. At the same time, the balance of both images Dp and Dq is adjusted so as to avoid collapsing the valley part.

[0126] The "multiply" described above is a term used in Photoshop (registered trademark) for a layer mode and is an OR operation in numerical processing. For example, a subdued red color provided with a hue of zero degree, a chroma saturation of 50 %, and a brightness of 80 %. When each color value of RGB is specified in the range of 0 to 255, RED is set to "204", GREEN is set to "102", and BLUE is set to "102". The HEX value (WEB color in hexadecimal / HTML color code) is set to #CC6666. Alternatively, the CMYK values used for color printing are approximately set to cyan "C 20 %", magenta "M 70 %", yellow "Y 50 %" and black "K 0 %".

[0127] The image, which is reduced in the color value by 50%, will be referred to as a red stereoscopic visualization image Gai'. The second synthesizer 15 synthesizes the slopegradient emphasis image Dr in which red is emphasized as the slope gradient increases and the synthesized image Dh obtained by synthesizing the over-ground openness layer Dp and the under-ground openness layer Dq by multiplication, and causes the display processor 280 to display the 5m-DEM red stereoscopic image Ki (completed 5m-mesh smooth red stereoscopic image GHi).

[0128] In other words, the memory 130 stores, as red stereoscopic image data Kmi (not illustrated), the area Ei (number), the 5m-mesh Mai (number), zri, the slope gradient ai, the color value of the slope gradient, the color value of the elevation-depression degree (not illustrated: over-ground openness, under-ground openness), and the like.

[0129] The multiply synthesizer 220 performs multiply synthesis of necessary data (color values) between the red stereoscopic image data Kmi [area Ei (number), 5m-mesh Mai (number), zri, slope gradient ai, color value of slope gradient da (5 m), color value of elevationdepression degree (not illustrated: over-ground openness, under-ground openness), and the like] and data of "completed 5m-mesh smooth red stereoscopic image GHi" [area Ei (number), 50m-mesh Mei (number), coordinates of four corners of 50m-mesh Mbi, fine 5m-mesh mei (number) included in 50m-mesh Mbi, division width da (approximately 5 m), bilinear interpolation value (zri), smoothing elevation value zhi, slope gradient, color value of slope gradient, elevation-depression degree (ridge-valley value), color value of elevation-depression degree, and the like] in the memory 240. The red stereoscopic image data Kmi is also simply referred to as a 5m-DEM red stereoscopic image Ki. In other words, the 5m-DEM red stereoscopic image Ki (completed 5m-mesh smooth red stereoscopic image GHi) and the completed 5m-mesh smooth red stereoscopic image GHi are synthesized by multiplication. The image obtained by multiply synthesis is also referred to as a tentative fractal stereoscopic image.

[0130] In other words, the multiply-synthesized image data KHi is made up of the red stereoscopic image data Kmi (5m-DEM red stereoscopic image Ki), data of the "completed 5m-mesh smooth red stereoscopic image GHi", a multiply synthesis value, a multiply-synthesized color value, and the like. The fractal stereoscopic image generator 260 (also referred to as a fractal stereoscopic image completion unit) generates, in a memory 270, fractal red stereoscopic completed DEM data created by associating color values when viewed from a predetermined direction with respect to the multiply-synthesized image data KHi in the memory 230.

[0131] In the present embodiment, a cluster (set) of the fractal red stereoscopic completed DEM data is referred to as a fractal red stereoscopic completed DEM. Figure 26 illustrates a fractal terrain stereoscopic image KGi representing the fractal red stereoscopic completed DEM being displayed on a screen by the display processor 280. Figure 26 is a partial enlargement of Figure 2.

[0132] A red stereoscopic image and the fractal terrain stereoscopic image KGi will be compared and described with reference to Figure 27. Figure 27(a) illustrates a general 5m-DEM red stereoscopic image Ki and Figure 27(b) illustrates the fractal terrain stereoscopic image KGi.

[0133] Comparing Figure 27(a) with Figure 27(b), Figure 27(b) has a fractal terrain stereoscopic image KGi that provides a greater stereoscopic effect in each step. In other words, the present embodiment thins (da1) (1 in 10: 5.555e-4) clusters of points of the 5m-DEM red stereoscopic image DEM (5.555e-5) illustrated in Figure 28(a), performs a conversion by thinning into a 50m-DEM (refer to Figure 28(b)), performs 10 x 10 bilinear interpolation and a 9 x 9 smoothing process (da2) with respect to the "50m-DEM thinned to 5m-DEM", and obtains the completed 5m-mesh smooth red stereoscopic image GHi illustrated in Figure 28(c).

[0134] Then, the completed 5m-mesh smooth red stereoscopic image GHi is read (da1), the 5m-DEM red stereoscopic image DEM (5.555e-5) illustrated in Figure 28(a) is read (db2), and the images are synthesized by multiplication (db1, db2) to obtain the fractal terrain stereoscopic image KGi illustrated in Figure 28(d).

[0135] In other words, although simply synthesizing the general (also referred to as normal) 5m-DEM red stereoscopic image Ki illustrated in Figure 29(a) and the 50m-DEM red stereoscopic image illustrated in Figure 29(b) by multiplication results in noticeable jaggies as shown in Figure 30, by performing multiply synthesis with a general red stereoscopic image after the 10 x 10 bilinear interpolation and the 9 x 9 moving average process described above, the present embodiment is capable of producing an image without any jaggies, which can be processed quickly, and which provides a greater stereoscopic effect as shown in Figure 2. In other words, the fractal terrain stereoscopic image KGi has improved visibility of both microtopography and large terrain.

[0136] Figure 31 is the fractal terrain stereoscopic image KGi of Mount Asama. As illustrated in Figure 31, Mount Asama, for example, appears to spit out significantly more than the surrounding terrain. Looking down from a distance, a stereoscopic effect is noticeable, and looking up closer, greater detail and a stereoscopic effect are noticeable. However, Figure 31 represents an example of a multiply synthesis of a 50cm-DEM and a 50m-DEM. To convert the 50cm-DEM to 50m-DEM, the 50cm-DEM is stored in a memory and subjected to two thinning processes by the thinning 50m-DEM converter 160.

[0138] While the embodiment described above has been described using a DEM of ground above ground, a DEM of the seabed ground may be used instead. In addition, visibility is greatly improved when printing on large floor carpets and when displaying large high-definition displays such as 8K.

[0139] Furthermore, the red stereoscopic image generator 120 and the blurred 5m-DEM smooth red stereoscopic image generator 250 may include an X-direction adjuster (not illustrated). The X-direction adjuster ensures that a width of a Y-direction (edge) of a large mesh is the same as a width of an X-direction (edge) when the red stereoscopic image generator 120 and the blurred 5m-DEM smooth red stereoscopic image generator 250 perform plane rectangular coordinate conversion. In other words, the X direction (edge) is moved upward (+ direction) so that the Y direction (edge in the longitudinal direction) equals the width of the X direction (edge in the latitudinal direction). This is referred to as an adjustment in the X direction.

[0140] The present invention can be used to create maps (including seabed).

[0141] < Second embodiment: (Lab colorization) > Images become clearer when a L*a*b* colorization process is applied to images (fractal terrain stereoscopic image KGi, 5m-DEM red stereoscopic map image Ki) according to the present first embodiment described above. The image subjected to L*a*b* colorization will be referred to as a L*a*b* color-imparted fractal image HKLi in the present embodiment. In addition, the computer processing system (program) will be referred to as a Lab color fractal terrain stereoscopic visualization image generation system in the present embodiment. For example, a valley becomes too dark. A water system is difficult to track. Ensure that it is not difficult to follow a channel of a valley due to being dark. The L*a*b* colorization process described above uses the "completed 50m-DEM thinned to 5m-DEM smoothed and blurred red DEM" and the red stereoscopic image 5m-DEM.

[0142] Figures 32 and 33 are schematic flowcharts explaining the Lab color fractal terrain stereoscopic visualization image generation system. In the present embodiment, since clusters of points of the base map (5m-DEM) are thinned and converted into a 50m-DEM and a 10m-DEM obtained by further miniaturization (superresolution) of the 50m-DEM is used (the 10m-DEM may be miniaturized to a 5m-DEM), the present embodiment will be described using symbols that differ from those of the first embodiment. As illustrated in Figure 32, a base map (5m-DEM (A)) defined using latitude and longitude of the Geospatial Information Authority stored in a memory is read (S10). In addition, a red stereoscopic image 5m-DEM is generated (S15). In other words, a 5m-DEM red stereoscopic map image Ki is generated based on the red stereoscopic image 5m-DEM.

[0143] In addition, clusters of points of the read 5m-DEM (base map (5m-DEM (A)) is thinned and converted into a 50m-DEM (S20). For example, a description will be given for 10 m (could be 5 m, 15 m, or 25 m) (in other words, 50 m divided by 5). By adopting 5 x 5 (10m-mesh), processing speed becomes about four times faster. Then, the 50m-DEM is subjected to 5 x 5 interpolation (TIN bilinear interpolation) to generate a fine fractal mesh of an approximately 10m-mesh (also referred to as a super resolution fractal fine 10m-mesh) (S30a). The DEM is referred to as a "50m-DEM thinned to 10m-DEM".

[0144] Next, a smoothing process is performed by applying a 5 x 5 moving average mesh (also referred to as a filter: 5 x 5 box average) to the "50m-DEM thinned to 10m-DEM" having been converted to super-resolution, and the smoothed image is displayed on a screen (S50a). The image after the moving average is referred to as a "50m-DEM thinned to 10m-DEM smoothed and blurred image AGai (not illustrated)" (also referred to as a 10m-DEM smoothed and blurred image). Then, an operator determines whether or not the "50m-DEM thinned to 10m-DEM smoothed and blurred image AGai" is smooth (S40a).

[0145] When it is determined that the image AGai is not smooth, the smoothing process of step S50a is performed once again. When it is determined in step S40a that the image AGai is smooth, the image AGai is stored in a memory as a "completed 50m-DEM thinned to 10m-DEM smoothed and blurred DEM", and using the "completed 50m-DEM thinned to 10m-DEM smoothed and blurred DEM", a red image (hereinafter, referred to as a completed 10m-mesh smooth image GHai: not illustrated) is generated in a memory (S60a). The completed 10m-mesh smooth image GHai (also simply referred to as a 10 m "blurred" Ru image) will be described later.

[0146] Then, the 5m-DEM red stereoscopic map image Ki generated in step S15 and the "completed 10m-mesh smooth image GHai" are synthesized by multiplication and color values are adjusted (S70a), and stored as a "fractal red stereoscopic completed DEM" in a memory (S80a). An image based on the "fractal red stereoscopic completed DEM" is referred to as a completed image of a fractal red relief image map (hereinafter, referred to as a completed fractal terrain stereoscopic visualization image HKGi (not illustrated)).

[0147] Then, as illustrated in Figure 32, a determination is made as to whether or not an instruction to perform L*a*b* colorization has been issued (S100). In step S100, when it is determined that L*a*b* colorization is to be performed, the L*a*b* color process illustrated in Figure 33 is performed. The L*a*b* color process includes a 10m-DEM Lab color adjustment image generation process (also referred to as a 10m-DEM superresolution Lab color adjustment image generation process), a 10m-DEM super-resolution L*a*b* color synthesis process, a 5m-DEM L*a*b* color adjustment image generation process (also referred to as a 5m-DEM normal L*a*b* color adjustment image generation process), and a 5m-DEM L*a*b* color synthesis process (also referred to as a normal L*a*b* color synthesis process).

[0148] As illustrated in Figure 33, the L*a*b* color process involves the 10m-DEM L*a*b* color adjustment image generation process (also referred to as the 10m-DEM super-resolution L*a*b* color adjustment image generation process) reading parameters including a slope gradient (also referred to as an inclination: second slope gradient), an over-ground openness (second over-ground openness), and an under-ground openness (second underground openness) when obtaining the "completed 10m-mesh smooth image GHai" (after super-resolution) obtained in step S60a (S400).

[0149] In the present embodiment, in order to distinguish an image of an over-ground openness of the 10m superresolution mesh from the first embodiment, an image of an over-ground openness of the 10m super-resolution mesh is simply referred to as an over-ground openness image Dpa in the present embodiment, an image of an under-ground openness of the 10m super-resolution mesh is simply referred to as an under-ground openness image Dqa in the present embodiment, and an image of a slope gradient (also referred to as an inclination) is simply referred to as a slope-gradient emphasis image Dra. The 10m-DEM Lab color adjustment image generation process (10m-DEM super-resolution Lab color adjustment image generation process) generates a "10m-mesh smooth Lab color image Lai (not illustrated)" by a moving average process similar to that described earlier (S420). The 10m-DEM Lab color adjustment image generation process (S420) involves reading image data of a superresolution mesh (10 m) of the over-ground openness image Dpa and obtaining a* data assigned to the a* channel for each read. In addition, image data of a super-resolution mesh (10 m) of the under-ground openness image Dqa is read and b* data assigned to the b* channel is obtained for each read. Furthermore, image data of the slope-gradient emphasis image Dra is read and L* data assigned to the L* channel is obtained for each read.

[0151] Every time a* data, b* data, and L* data are obtained, the data is defined in a L*a*b* space to obtain the "10m-mesh smooth Lab color image Lai (not illustrated)". Then, the 10m-DEM super-resolution Lab color synthesis process synthesizes (synthesizes by multiplication) the "10m-mesh smooth Lab color image Lai (also referred to as a super-resolution smooth Lab color-imparted image)" and the "completed 10m-mesh smooth image GHai" (after super-resolution) in step S60a to obtain the "10m-mesh smooth Lab color-imparted image HLai (also referred to as a second L*a*b* color-imparted stereoscopic visualization image)" illustrated in Figures 34 and 41 (S440). Since the "10m-mesh smooth Lab color image Lai" is similar to the 5m-DEM Lab color-imparted image Lbi (also referred to as a normal Lab color-imparted image or a first L*a*b* color-imparted stereoscopic visualization image) in Figures 34 and 43 to be described later, the "10m-mesh smooth Lab color image Lai" is not illustrated. In addition, Figure 41 illustrates an enlarged view of the "10m-mesh smooth Lab color-imparted image HLai". Transparency of the "10m-mesh smooth Lab color image Lai" of the "10m-mesh smooth Lab color-imparted image HLai" is set to about 10%.

[0152] On the other hand, the 5m-DEM Lab color adjustment image generation process (5m-DEM normal Lab color adjustment image generation process) determines an overground openness and an under-ground openness to determine a ridge-valley value (also referred to as an elevationdepression degree) for each 5m-mesh in order to obtain the 5m-DEM red stereoscopic map image Ki (not converted to super-resolution) generated in step S15. Note that a gradation color value (reddish color) indicating a color value of a combination of the ridge-valley value and the slope gradient (also referred to as inclination) is assigned to the 5m-mesh. In the present embodiment, an image of an overground openness when obtaining the 5m-DEM red stereoscopic map image Ki is simply referred to as an over-ground openness image Dp, an image of an underground openness is simply referred to as an under-ground openness image Dq in the present embodiment, and an image of a slope gradient is simply referred to as a slopegradient emphasis image Dr in a similar manner to the first embodiment.

[0153] Then, the 5m-DEM L*a*b* color adjustment image generation process (5m-DEM normal L*a*b* color adjustment image generation process) similarly generates a 5m-DEM L*a*b* color-imparted image Lbi (refer to Figures 34 and 43) in which the over-ground openness has been converted into a* of L*a*b* color, the under-ground openness has been converted into b*, and the slope gradient (also referred to as an inclination) has been converted into L* (S460). Then, the 5m-DEM L*a*b* color synthesis process obtains a 5m-DEM L*a*b* color image KLi created by synthesizing (synthesizing by multiplication) the 5m-DEM red stereoscopic map image Ki (not converted to superresolution) and the 5m-DEM L*a*b* color-imparted image Lbi (normal Lab color-imparted image: refer to Figures 34 and 43) (S480: refer to Figures 34 and 42). The 5m-DEM L*a*b* color-imparted image Lbi of the 5m-DEM L*a*b* color image KLi in Figures 34 and 42 is an example of setting the transparency of L*a*b* to 20%.

[0154] Then, the image synthesis process synthesizes (synthesizes by multiplication) the "10m-mesh smooth L*a*b* color-imparted image HLai (super-resolution smooth Lab color-imparted image)" in step S440 and the 5m-DEM L*a*b* color-imparted image Lbi (normal Lab color-imparted image) in step S480 to generate the L*a*b* color-imparted fractal image HKLi (refer to Figures 34 and 44) (S500), and displays the L*a*b* color-imparted fractal image HKLi (S520). More specifically, the synthesized image is considered a tentative L*a*b* color-imparted fractal image, and color values as viewed from a predetermined direction (the direction of the sun) are adjusted with respect to the tentative L*a*b* color-imparted fractal image to obtain the L*a*b* color-imparted fractal image HKLi. Figure 35 is a schematic configuration diagram of the Lab color fractal terrain stereoscopic visualization image generation system. Descriptions of parts in Figure 35 that are denoted by the same reference numerals as described above will not be repeated.

[0155] The present embodiment includes a L*a*b* color unit 320, a synthesizer 340, and the like in addition to the respective system components illustrated in Figure 3 and described above. Note that the blurred 5m-DEM smooth red stereoscopic image generator 250 is referred to as a blurred and smoothed red stereoscopic image generator 251 in the present embodiment. In addition, the "blurred 5m-DEM smoothed data hgi" will be referred to as "superresolution smoothed data hgi'". In addition, it is assumed that the "completed 10m-mesh smooth image GHai (after super-resolution)" has been generated in the memory 240. Furthermore, it is assumed that the 5m-DEM red stereoscopic image Ki (red stereoscopic image data Kmi) has been generated in the memory 130.

[0156] The blurred and smoothed red stereoscopic image generator 251 performs a red stereoscopic visualization process ("blurred" stereoscopic visualization process) using the "super-resolution smoothed data hgi'". In other words, for each subject point, a ridge-valley value between the subject point and an mbi adjacent to the subject point is determined, and the "completed 10m-mesh smooth image GHai" (after super-resolution) that is a set of data in which a gradation color value indicating a color value of a combination of the ridge-valley value and a slope gradient is assigned to the mbi of the subject point is generated in the memory 240.

[0157] The L*a*b* color unit 320 (Lab color process) includes a 10m-DEM L*a*b* color adjustment image generation process (also referred to as a 10m-DEM superresolution L*a*b* color adjustment image generation process), a 10m-DEM super-resolution L*a*b* color synthesis process, a 5m-DEM L*a*b* color adjustment image generation process (also referred to as a 5m-DEM normal L*a*b* color adjustment image generation process), and a 5m-DEM L*a*b* color synthesis process (also referred to as a normal L*a*b* color synthesis process). The 10m-DEM L*a*b* color adjustment image generation process converts a super-resolution over-ground openness (parameter) of the over-ground openness image Dpa when obtaining the "completed 10m-mesh smooth image GHai (after super-resolution)" into a* of L*a*b* color, converts a super-resolution under-ground openness (parameter) of the under-ground openness image Dqa into b*, and converts a super-resolution slope gradient (parameter: also referred to as an inclination) of the slope-gradient emphasis image Dra into L*, and stores the converted parameters in a memory (not illustrated).

[0158] Then, by defining the data in a L*a*b* space, the "10m-mesh smooth L*a*b* color image Lai" (refer to Figure 43) is obtained and stored in a memory (not illustrated). The 10m-DEM super-resolution L*a*b* color synthesis process synthesizes (synthesizes by multiplication) the "10m-mesh smooth L*a*b* color image Lai (super-resolution smooth L*a*b* color-imparted image)" and the "completed 10m-mesh smooth image GHai (after super-resolution)" to obtain the "10m-mesh smooth L*a*b* color-imparted image HLai" illustrated in Figures 34 and 41 (refer to Figure 41). On the other hand, the 5m-DEM L*a*b* color adjustment image generation process (5m-DEM normal L*a*b* color adjustment image generation process) generates a 5m-DEM L*a*b* color-imparted image Lbi (refer to Figure 43) in which the over-ground openness has been converted into a* of L*a*b* color, the under-ground openness has been converted into b*, and the slope gradient (inclination) has been converted into L*.

[0159] Then, the 5m-DEM L*a*b* color synthesis process obtains a 5m-DEM L*a*b* color image KLi created by synthesizing (synthesizing by multiplication) the 5m-DEM red stereoscopic map image Ki (not converted to superresolution) and the 5m-DEM L*a*b* color-imparted image Lbi (normal L*a*b* color-imparted image: refer to Figures 34 and 43) (refer to Figures 34 and 42). The synthesizer 340 (image synthesis process) synthesizes (synthesizes by multiplication) the "10m-mesh smooth L*a*b* color-imparted image HLai (super-resolution smooth L*a*b* color-imparted image)" and the 5m-DEM L*a*b* color-imparted image Lbi (normal L*a*b* color-imparted image) to generate a L*a*b* color-imparted fractal image HKLi (refer to Figures 34 and 44) in a memory 360. More specifically, the synthesized image is considered a tentative L*a*b* color-imparted fractal image, and color values as viewed from a predetermined direction (the direction of the sun) are adjusted with respect to the tentative L*a*b* color-imparted fractal image to obtain the L*a*b* color-imparted fractal image HKLi, which is then stored in the memory 360.

[0160] A display processor 200 includes a display memory (not illustrated), reads data in accordance with an entered image type into the display memory, and displays images (for example, the L*a*b* color-imparted fractal image HKLi and the "completed 10m-mesh smooth image GHai" (after super-resolution)) of a color value assigned to the data on the screen of the display.

[0161] The description of the L*a*b* color unit 320 provided earlier will be supplemented using Figure 36. Figure 36 is a schematic configuration diagram of the L*a*b* color unit 320. However, the memory 130, the synthesizer 340, a fine tuning corrector 72, and the like are described.

[0162] As illustrated in Figure 36, the L*a*b* color unit 320 includes an inclination image gradation corrector 62, an over-ground openness image gradation corrector 64, an under-ground openness image gradation corrector 63, an L* channelizing unit 66, a b* channelizing unit 65, an a* channelizing unit 67, an L*a*b* color imaging unit 68, a gradation corrector 69, an XYZ color system converter 71, an RGB color system converter 70, the fine tuning corrector 72, an inclination spectrum calculator 52, an under-ground openness spectrum calculator 51, an overground openness spectrum calculator 53, and the like and adjusts images so that valleys and depressions with a high under-ground openness become cyan while ridges and peaks with a high over-ground openness become red. Valley slopes and the like with low over-ground openness are greenish in color.

[0163] Images ("10m-mesh smooth Lab color-imparted image HLai", L*a*b* color-imparted fractal image HKLi) are obtained which solve a problem in that valleys are dark and difficult to see by adjusting and improving the expression of valleys that are too dark to be seen to cyanish colors. A dark cyan color gives a sense of depth. However, transparency is adjusted.

[0164] The inclination spectrum calculator 52 calculates a spectrum distribution (also referred to as a slopegradient spectrum) of the super-resolution slope-gradient emphasis image Dra in the memory 130 and stores the calculated spectrum distribution in a memory 55.

[0165] The slope-gradient spectrum of the super-resolution slope-gradient emphasis image Dr is shown in Figure 37(a) as a histogram with the slope gradient (0 degree to 90 degrees) on an axis of abscissa and pixel frequency (n) on an axis of ordinate. As illustrated in Figure 37(a), the slope gradient ai is essentially distributed between 0 degree and 50 degrees.

[0166] The over-ground openness spectrum calculator 53 calculates a spectrum distribution (also referred to as an over-ground openness spectrum) of the super-resolution over-ground openness emphasis image Dp in the memory 130 and stores the calculated spectrum distribution in a memory 54.

[0167] The over-ground openness spectrum is shown in Figure 37(b) as an over-ground openness histogram with openness (0 degree to 180 degrees) on an axis of abscissa and pixel frequency (n) on an axis of ordinate. As illustrated in Figure 37(b), the under-ground openness 0i is essentially distributed between 0 degree and 90 degrees (center is 90 degrees: side of 90 degrees to 130 degrees is sharp).

[0168] The under-ground openness spectrum calculator 51 calculates a spectrum distribution (also referred to as an under-ground openness spectrum) of the superresolution under-ground openness emphasis image Dq in the memory 130 and stores the calculated spectrum distribution in a memory 53. The under-ground openness spectrum is shown in Figure 37(c) as an over-ground openness histogram with under-ground openness (0 degree to 180 degrees) on an axis of abscissa and pixel frequency (n) on an axis of ordinate. As illustrated in Figure 37(c), the underground openness ^i is essentially distributed between 50 degrees and 130 degrees (center is 90 degrees: side of 50 degrees to 90 degrees is sharp).

[0169] (Description of image gradation unit) The inclination image gradation corrector 62 corrects gradation so that the steeper the slope, the darker the color. In other words, a linear conversion is performed in which an input side (axis of abscissa) is set to slope gradient 0 degree to slope gradient 50 degrees, an output side is set to 0 (black) to 255 (white), and a slope gradient ai of 50 degrees is converted to "0" while a slope gradient ai of 0 degree is converted into a maximum value of 255 (see Figure 38(a)). Specifically, a look-up table is used.

[0170] A histogram of slope gradient obtained by the conversion described above is illustrated in Figure 37(a). The over-ground openness image gradation corrector 63 corrects gradation so that ridgelines are light. In other words, a linear conversion is performed in which an input side (axis of abscissa) is set to over-ground openness 50 degrees to over-ground openness 130 degrees, an output side is set to 0 (black) to 255 (white), and an over-ground openness 0i of 50 degrees is converted to "0" while an over-ground openness 0i of 130 degrees is converted into a maximum value of 255 (see Figure 38(b)). However, conversion is performed to "120 degrees" when the over-ground openness 0i is 90 degrees. Specifically, a look-up table is used. In other words, as illustrated in Figure 38(b), a center of the conversion line passes through (90 degrees, 120). An over-ground histogram obtained by the conversion described above is illustrated in Figure 37(b).

[0171] The under-ground openness image gradation corrector 64 corrects gradation so that a line of a valley is dark. In other words, a linear conversion is performed in which an input side (axis of abscissa) is set to under-ground openness 50 degrees to under-ground openness 130 degrees, an output side is set to 0 (black) to 255 (white), and an under-ground openness ai of 50 degrees is converted to "255" while an under-ground openness ai of 130 degrees is converted into "0" (see Figure 38(c)). However, conversion is performed to "120" to the output when the under-ground openness ai is 90 degrees. Specifically, a look-up table is used. A histogram of under-ground openness obtained by the conversion described above is illustrated in Figure 37(c).

[0172] In other words, a relationship between over-ground openness and under-ground openness by the gradient conversion unit is illustrated in Figure 39 as a scatter diagram. Figure 39 plots the over-ground openness (50 degrees to 130 degrees) on an axis of abscissa and the under-ground openness (50 degrees to 130 degrees) on an axis of ordinate. The scatter diagram is centered on (90 degrees, 90 degrees). The scatter diagram shows that the closer to the straight line, more blue, the further away from the straight line, more yellow, and even further away, more red.

[0173] The color of the plot points indicates a color corresponding to an amount of inclination of a same subject point. Figure 39 shows that there is an inversely proportional relationship between over-ground openness and under-ground openness. This relationship becomes stronger the shorter the distance. The overground openness is large and the under-ground openness is small in ridge areas while the over-ground openness is small and the under-ground openness is large in valley areas. The color of the plot points indicate that there is a weak proportional relationship between the sum of overground openness and under-ground openness and inclination.

[0174] (Channelizing unit) Every time the inclination image gradation corrector 62 converts a slope gradient (0 degree to 50 degrees) into a color value (255 to 0), the L* channelizing unit 66 assigns the color value to the L* channel (see Figure 38(a)).

[0175] Every time the over-ground openness image gradation corrector 63 converts an over-ground openness 0i (50 degrees to 130 degrees) into a color value (0 to 255), the a* channelizing unit 67 assigns the color value to the a* channel.

[0176] Every time an under-ground openness ^i (50 degrees to 130 degrees) is converted into a color value (255 to 0), the b* channelizing unit 65 assigns the color value to the b* channel.

[0177] The L*a*b* color imaging unit 68 defines L* data of the L* channelizing unit 66, a* data of the a* channelizing unit 67, and b* data of the b* channelizing unit 65 in the L*a*b* space and obtains a super-resolution Lab color image Li (Lai, Lbi) in a memory 41 (see Figure 43).

[0178] (Other) While the corrector 69 may define L*a*b* color images Li (Lai, Lbi) in the RGB space to be synthesized with the 5m-DEM red stereoscopic map image Ki and the completed 10m-mesh smooth image GHai, since the L*a*b* color images Li have a wider color space than the RGB space, the corrector 69 uses a toe curve to perform fine adjustment after approximate color adjustment is performed by level correction.

[0179] For example, a slope gradient of 0 degrees to 50 degrees is changed to 0 degree to 30 degrees or 0 degree to 70 degrees to be reassigned a color value. In addition, an over-ground openness (50 degrees to 130 degrees) or an under-ground openness (50 degrees to 130 degrees) is changed to 60 degrees to 120 degrees or 70 degrees to 110 degrees to be reassigned a color value.

[0180] The XYZ color system converter 71 converts a Lab adjusted image into the XYZ color system (defines in a color space memory of the XYZ color system) (Lab image of XYZ color system).

[0181] The RGB color system converter 71 converts a Lab image in the XYZ color system into the RGB color system (defines in a RGB space memory) (Lab image of RGB layer). The Lab image of the RGB layer is stored in a memory 42.

[0182] The synthesizer 340 (image synthesis process) superimposes the "10m-mesh smooth L*a*b* color-imparted image HLai (super-resolution smooth L*a*b* color-imparted image)", the 5m-DEM L*a*b* color-imparted image Lbi (normal L*a*b* color-imparted image), and the Lab image of the RGB layer on the red stereoscopic visualization image (5m-DEM red stereoscopic image Ki, fractal terrain stereoscopic image KGi) stored in a file 40. The image is referred to as a L*a*b* fractal stereoscopic image HLai and is stored in the memory 360. However, color values as viewed from a predetermined direction (the direction of the sun) are adjusted with respect to the tentative L*a*b* color-imparted fractal image to obtain the L*a*b* color-imparted fractal image HKLi.

[0183] The fine tuning corrector 72 adjusts a contrast (transparency) or the like of the L*a*b* color (by operator input). In other words, by overlapping and synthesizing these images, the expression of valleys that have become too dark is adjusted and improved to cyanish colors. Therefore, the valley is neither dark nor difficult to see.

[0184] <Third embodiment> The third embodiment represents a method of emphasizing water systems. Figure 40 is a schematic configuration diagram of the third embodiment. Duplicate explanations are omitted for parts denoted by the same reference numerals as described above. As illustrated in Figure 40, a water system adjuster 45 is provided. The water system adjuster 45 skips the bright side of a histogram of under-ground openness and adjusts an image to be only on the dark side. Accordingly, a portion where under-ground openness is high (portion relatively lower than valley portions or periphery) is extracted.

[0185] Then, in a similar manner to the second embodiment, the L*a*b* color image Li and the red stereoscopic image are superimposed. Note that unlike contour maps and the like, a red relief image map has no concept of height and only expresses unevenness. Therefore, when there is a large difference in elevation within a target area, an overall orthostatic sensation may not be sufficient. When a large terrain is represented on a red relief image map, this can be achieved by increasing the range of consideration of the degree of openness according to the scale of the represented terrain (in other words, when desiring to see terrain undulations in an area of about 1 km, a range of the degree of openness is set to 1000 m).

[0186] However, in practice, the calculation of the degree of openness is regulated by microtopography that exists around a location of interest and the degree of openness of 1 km ahead is seldom calculated. For example, if a range of the degree of openness such as 1 km is set with respect to 1m-DEM, the value of the degree of openness will be saturated in valley and ridge portions, causing the valleys to become too dark and the ridges to become too light.

[0187] This problem is solved by reducing the resolution of the DEM to be calculated (reducing the resolution of the terrain) and then performing a calculation. This allows for calculations that take the earth system into account (see Figure 45). Between 1m-DEM and 4m-DEM, 4m-DEM provides a stronger sense of undulation as a whole.

[0188] In addition, the methods according to the embodiments described above can be applied to the topography of Venus and Mars. Furthermore, it can also be applied to the visualization of unevenness measured with an electron microscope. When applied to gaming devices, it provides a stereoscopic effect without the need for glasses. While a super-resolution image is generated using an elevation-depression degree (ridge-valley value) obtained from over-ground openness and under-ground openness in the embodiments described above, the super-resolution image may be superimposed and displayed on an image obtained by sky coverage, a topographic protection factor, a plane curvature, a high-bass filter, or a Mexican hat function. Alternatively, an image can be created by inverting the sky coverage, the topographic protection factor, the plane curvature, the high-bass filter, the Mexican hat function, or the like and the image can be adopted as an under-ground openness image. Note that the DEM of a base map may be ALB (Airborne lidar Bathymetry) (point cloud density: 1 point / m2). [Reference Signs List] 110 ground map memory 120 red stereoscopic image generator 140 area reading unit 160 thinning 50m-DEM converter 180 miniaturization processor 210 moving average unit 220 multiply synthesizer 250 blurred 5m-DEM smooth red stereoscopic image generator 260 fractal stereoscopic image generator 280 display processor

Claims

1. A fractal terrain stereoscopic visualization image generation system, comprising:a storage that stores a DEM of terrain defined by a mesh of a certain size as a digital elevation model;(A). means of obtaining a first elevation-depression degree using a first over-ground openness and a first under-ground openness based on a DEM of terrain of a predetermined area of the digital elevation model and a DEM of terrain within a consideration distance, further obtaining a first slope gradient, and generating a stereoscopic visualization image in a first gradation color of a combination of the first elevation-depression degree and the first slope gradient;(B). means of generating a large mesh several times larger than a mesh of the DEM of the terrain of the predetermined area, and reading a certain number of clusters of points of the DEM of the terrain in a thinned manner into the large mesh to generate a low-density large mesh DEM;(C). means of applying an interpolation process to the low density large mesh to generate miniaturized low-density fine meshes, and assigning an interpolated elevation value to each low-density fine mesh;(D). means of sequentially performing a moving average process for each low-density fine mesh to determine a moving average of the interpolated elevation values;(E). means of designating each low-density fine mesh as a subject point after a moving average process is performed by the means (D), obtaining a second elevationdepression degree using a second over-ground openness and a second under-ground openness based on a moving-averaged elevation value of the low-density fine mesh at the subject point and a moving-averaged elevation value of the low-density fine mesh within the consideration distance, further obtaining a second slope gradient, and generating an image of a combination of the second elevation-depression degree and the second slope gradient to which a second gradation color is assigned as a "blurred" stereoscopic visualization image; and(F). means of synthesizing the stereoscopic visualization image and the "blurred" stereoscopic visualization image by multiplication and outputting the synthesized image as a fractal terrain stereoscopic visualization image.

2. The fractal terrain stereoscopic visualization image generation system according to claim 1, whereincauses the fractal terrain stereoscopic visualization image to apply a color-adjusted color value when viewed from a predetermined direction to the low-density fine mesh.

3. The fractal terrain stereoscopic visualization image generation system according to claim 1, comprising:in a case of a large mesh that is tens of times larger than the DEM mesh of the terrain,(G). means of causing the means of (B) to repeat thinning of a certain number of clusters of points of the DEM of the terrain a predetermined number of times.

4. The fractal terrain stereoscopic visualization image generation system according to claim 1, whereinthe means of (C)equally divides an edge in a latitudinal direction and an edge in a longitudinal direction of the low-density large mesh into ten parts and performs an interpolation process, andthe means of (D) determines a moving average by applying a 9 x 9 moving average filter to the low-density large mesh after the interpolation.

5. The fractal terrain stereoscopic visualization image generation system according to claim 1, whereinreads an elevation value of the low-density fine mesh after the moving average process to a display memory and displays as a smooth image on a screen, and performs the moving average process once again with an input of an instruction to determine a moving average.

6. The fractal terrain stereoscopic visualization image generation system according to claim 1, wherein the color tone of the first slope gradient and the second slope gradient is displayed in a reddish color.

7. The fractal terrain stereoscopic visualization image generation system according to claim 1, wherein the DEM of the digital model is a 50cm-DEM, a 1m-DEM, a 5m-DEM, or a 10m-DEM.

8. The fractal terrain stereoscopic visualization image generation system according to claim 1, comprising:(H). means of defining the first over-ground openness, the first under-ground openness, and the first slope gradient in an L*a*b* space to generate a L*a*b* color image;(I). means of generating a first L*a*b* color-imparted stereoscopic visualization image by synthesizing the L*a*b* color image with the stereoscopic visualization image;(J). means of defining the second over-ground openness, the second under-ground openness, and the second slope gradient in an L*a*b* space to generate a second L*a*b* color-imparted stereoscopic visualization image; and(K). means of generating a L*a*b* color-imparted fractal terrain stereoscopic visualization image by synthesizing the first L*a*b* color-imparted stereoscopic visualization image and the second L*a*b* color-imparted stereoscopic visualization image with the "blurred" stereoscopic visualization image and displaying the L*a*b* color-imparted fractal terrain stereoscopic visualization image.

9. A fractal terrain stereoscopic visualization image generation program, causing a computer to execute functions as:means of causing a storage to store a DEM of terrain defined by a mesh of a certain size as a digital elevation model;(A). means of obtaining a first elevation-depression degree using a first over-ground openness and a first under-ground openness based on a DEM of terrain of a predetermined area of the digital elevation model and a DEM of terrain within a consideration distance, further obtaining a first slope gradient, and generating a stereoscopic visualization image in a first gradationcolor of a combination of the first elevation-depression degree and the first slope gradient;(B). means of generating a large mesh several times larger than a mesh of the DEM of the terrain of the predetermined area, and reading a certain number of clusters of points of the DEM of the terrain in a thinned manner into the large mesh to generate a low-density large mesh DEM;(C). means of applying an interpolation process to the low density large mesh to generate miniaturized low-density fine meshes, and assigning an interpolated elevation value to each low-density fine mesh;(D). means of sequentially performing a moving average process for each low-density fine mesh to determine a moving average of the interpolated elevation values;(E). means of designating each low-density fine mesh as a subject point after a moving average process is performed by the means (D), obtaining a second slope gradient and a second ridge-valley value based on a moving-averaged elevation value of the low-density fine mesh at the subject point and a moving-averaged elevation value of the low-density fine mesh within the consideration distance, and generating an image of a combination of the second elevation-depression degree and the second slope gradient to which a second gradationcolor is assigned as a "blurred" stereoscopic visualization image; and(F). means of synthesizing the stereoscopic visualization image and the "blurred" stereoscopic visualization image by multiplication and outputting the synthesized image as a fractal terrain stereoscopic visualization image.

10. The fractal terrain stereoscopic visualization image generation program according to claim 9, whereinthe means of (F)causes the fractal terrain stereoscopic visualization image to apply a color-adjusted color value when viewed from a predetermined direction to the low-density fine mesh.

11. The fractal terrain stereoscopic visualization image generation program according to claim 9, causing a computer to execute functions as:in a case of a large mesh that is tens of times larger than the DEM mesh of the terrain,(G). means of causing the means of (B) to repeat thinning of a certain number of clusters of points of the DEM of the terrain a predetermined number of times.

12. The fractal terrain stereoscopic visualization image generation program according to claim 9, causing a computer to execute:the means of (C) whichequally divides an edge in a latitudinal direction and an edge in a longitudinal direction of the low-density large mesh into ten parts and performs the interpolation process, andthe means of (D) which determines a moving average by applying a 9 x 9 moving average filter to the low-density large mesh after the interpolation.

13. The fractal terrain stereoscopic visualization image generation program according to claim 9, causing a computer to execute:the means of (C) which furtherreads an elevation value of the low-density fine mesh after the moving average process to a display memory and displays as a smooth image on a screen, and performs the moving average process once again with an input of an instruction to determine a moving average.

14. The fractal terrain stereoscopic visualization image generation program according to claim 9, causing a computer to:display the color tone of the first slope gradient and the second slope gradient in a reddish color.

15. The fractal terrain stereoscopic visualization image generation program according to claim 9, causing a computer to:store a 50cm-DEM, a 1m-DEM, a 5m-DEM, or a 10m-DEM as a DEM of the digital model in a storage.

16. The fractal terrain stereoscopic visualization image generation program according to claim 9, causing a computer to function as:(H). means of defining the first over-ground openness, the first under-ground openness, and the first slope gradient in an L*a*b* space to generate a L*a*b* color image;(I). means of generating a first L*a*b* color-imparted stereoscopic visualization image by synthesizing the L*a*b* color image with the stereoscopic visualization image;(J). means of defining the second over-ground openness, the second under-ground openness, and the second slope gradient in an L*a*b* space to generate a second L*a*b* color-imparted stereoscopic visualization image; and(K). means of generating a L*a*b* color-imparted fractal terrain stereoscopic visualization image by synthesizing the first L*a*b* color-imparted stereoscopic visualization image and the second L*a*b* color-impartedstereoscopic visualization image with the "blurred" stereoscopic visualization image and displaying the L*a*b* color-imparted fractal terrain stereoscopic visualization image.

Images