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Sky-View Factor as a Relief Visualization Technique

Zakšek, Klemen, Oštir, Kristof, Kokalj, Žiga·2011·Remote Sensing
DOI 10.3390/rs3020398
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Abstract

Remote sensing has become the most important data source for the digital elevation model (DEM) generation. DEM analyses can be applied in various fields and many of them require appropriate DEM visualization support. Analytical hill-shading is the most frequently used relief visualization technique. Although widely accepted, this method has two major drawbacks: identifying details in deep shades and inability to properly represent linear features lying parallel to the light beam. Several authors have tried to overcome these limitations by changing the position of the light source or by filtering. This paper proposes a new relief visualization technique based on diffuse, rather than direct, illumination. It utilizes the sky-view factor—a parameter corresponding to the portion of visible sky limited by relief. Sky-view factor can be used as a general relief visualization technique to show relief characteristics. In particular, we show that this visualization is a very useful tool in archaeology as it improves the recognition of small scale features from high resolution DEMs.

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Figure 1 Angle dependence of analytical hill-shading: 315° azimuth illumination (a) , and 45° azimuth (b) . Note the lower right corner of both plates where the relief structures have a main direction of about 45°. Almost no relief details can be observed in (b) because the structures lie in the direction of shading. The image shows ring and cone structures, and dunes in Athabasca Valles, Mars (data from HiRISE instrument aboard Mars Reconnaissance Orbiter [1]; credits to NASA/JPL/University of Arizona).
Figure 1 Angle dependence of analytical hill-shading: 315° azimuth illumination (a) , and 45° azimuth (b) . Note the lower right corner of both plates where the relief structures have a main direction of about 45°. Almost no relief details can be observed in (b) because the structures lie in the direction of shading. The image shows ring and cone structures, and dunes in Athabasca Valles, Mars (data from HiRISE instrument aboard Mars Reconnaissance Orbiter [1]; credits to NASA/JPL/University of Arizona).CC-BY · From the PDF
Figure 2 The sky-view factor is defined by the part of the visible sky ( Ω ) above a certain observation point as seen from a two-dimensional representation (a) . The algorithm computes the vertical elevation angle of the horizon γ i in n (eight are presented here) directions to the specified radius R (b) .
Figure 2 The sky-view factor is defined by the part of the visible sky ( Ω ) above a certain observation point as seen from a two-dimensional representation (a) . The algorithm computes the vertical elevation angle of the horizon γ i in n (eight are presented here) directions to the specified radius R (b) .CC-BY · From the PDF
Figure 3 An example of the SVF based visualization for the volcanic region in Tanzania. Olmoti (below left), Embagai (in the middle) and small Kerimasi craters (above right) are visible. We tested visualizations based on a celestial hemisphere (a) and a celestial sphere ( b) . When considering the whole sphere, the results have an increased range (SVF can theoretically reach a value of 2). This brings improvement in automatic detection of linear structures because the method exposes many edges. The results however, are not intuitive because slopes are visualized in the same manner as a horizontal plane. Therefore, it is difficult to distinguish between them.
Figure 3 An example of the SVF based visualization for the volcanic region in Tanzania. Olmoti (below left), Embagai (in the middle) and small Kerimasi craters (above right) are visible. We tested visualizations based on a celestial hemisphere (a) and a celestial sphere ( b) . When considering the whole sphere, the results have an increased range (SVF can theoretically reach a value of 2). This brings improvement in automatic detection of linear structures because the method exposes many edges. The results however, are not intuitive because slopes are visualized in the same manner as a horizontal plane. Therefore, it is difficult to distinguish between them.CC-BY · From the PDF
Figure 4 Scatterplots comparing SVF estimated in 8 and 16 directions (red), and 32 and 64 directions (yellow).
Figure 4 Scatterplots comparing SVF estimated in 8 and 16 directions (red), and 32 and 64 directions (yellow).CC-BY · From the PDF
Image 5
CC-BY · From the PDF
Figure 6 Visualization of dolines in a karstic area (Slovenia). The elevation model has a spatial resolution of 1 m. SVF was computed in 16 directions and a search radius of 30 m was used (similar to the diameter of the dolines).
Figure 6 Visualization of dolines in a karstic area (Slovenia). The elevation model has a spatial resolution of 1 m. SVF was computed in 16 directions and a search radius of 30 m was used (similar to the diameter of the dolines).CC-BY · From the PDF
Figure 7 A 20 m long profile of DEM created from laser scanning data over the archaeological site at Tonovcov grad (Slovenia); see Figure 8 for the profile position. We compare values of SVF (range 0 to 1), analytical hill-shading (cosine of incidence angle, 0 to 1) and elevation (395 to 405 m). We also tested transects extending in other directions to consider the effect of directional illumination, but the results of analytical hill-shading were always noisier than the SVF.
Figure 7 A 20 m long profile of DEM created from laser scanning data over the archaeological site at Tonovcov grad (Slovenia); see Figure 8 for the profile position. We compare values of SVF (range 0 to 1), analytical hill-shading (cosine of incidence angle, 0 to 1) and elevation (395 to 405 m). We also tested transects extending in other directions to consider the effect of directional illumination, but the results of analytical hill-shading were always noisier than the SVF.CC-BY · From the PDF
Figure 8 A comparison of analytical hill-shading (a) and SVF based visualization (b) of a DEM generated from lidar data (0.5 m spatial resolution), for the archaeological site at Tonovcov grad (Slovenia). The remains of the building outlines and defense walls are clearly recognizable on the SVF based visualization. The profile location from Figure 7 is represented by the red line. SVF was computed in 16 directions with a 10 m search radius.
Figure 8 A comparison of analytical hill-shading (a) and SVF based visualization (b) of a DEM generated from lidar data (0.5 m spatial resolution), for the archaeological site at Tonovcov grad (Slovenia). The remains of the building outlines and defense walls are clearly recognizable on the SVF based visualization. The profile location from Figure 7 is represented by the red line. SVF was computed in 16 directions with a 10 m search radius.CC-BY · From the PDF
Figure 9 Even though the rugged relief is appropriately represented by hill-shading, as shown in (a) , the legibility of its features is enhanced with the use of SVF (b) , and even more so when both methods are combined (c) . The combination is especially suitable for cartographic purposes. Grand Canyon, Arizona, USA. Data source USGS (1' NED). SVF was computed in 16 directions with a 300 m search radius.
Figure 9 Even though the rugged relief is appropriately represented by hill-shading, as shown in (a) , the legibility of its features is enhanced with the use of SVF (b) , and even more so when both methods are combined (c) . The combination is especially suitable for cartographic purposes. Grand Canyon, Arizona, USA. Data source USGS (1' NED). SVF was computed in 16 directions with a 300 m search radius.CC-BY · From the PDF
Figure 10 A comparison of visualization techniques used in archaeological interpretation for the area of Zagrajec Hill Fort, near Komen, Slovenia, a known yet un-excavated site. The images are produced by: standard analytical hill-shading (45° zenith angle, 315° azimuth; (a) ); the mean of analytical hill-shadings from 16 directions (b) ; a RGB composite of the first three components of the principal component analysis of analytical hill-shadings from 16 directions (c) ; trend removal and height coding by modulo distribution (±1 m, (d) ); Sobel edge detection (e) ; sky-view factor (10 m search radius in 16 directions, (f) ).
Figure 10 A comparison of visualization techniques used in archaeological interpretation for the area of Zagrajec Hill Fort, near Komen, Slovenia, a known yet un-excavated site. The images are produced by: standard analytical hill-shading (45° zenith angle, 315° azimuth; (a) ); the mean of analytical hill-shadings from 16 directions (b) ; a RGB composite of the first three components of the principal component analysis of analytical hill-shadings from 16 directions (c) ; trend removal and height coding by modulo distribution (±1 m, (d) ); Sobel edge detection (e) ; sky-view factor (10 m search radius in 16 directions, (f) ).CC-BY · From the PDF

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Metadata

Authors
Zakšek, K., Oštir, K., & Kokalj, Ž.
Affiliations
  • Institute of Geophysics, University of Hamburg, Bundesstr. 55, D-20146 Hamburg, Germany
  • Centre of Excellence Space-SI, Aškerčeva cesta 12, SI-1000 Ljubljana, Slovenia
Venue
Remote Sensing
Publisher
MDPI AG
Published
2011-02-23
Volume
3
Issue
2
Pages
398-415
ISSN
2072-4292
DOI
10.3390/rs3020398
Cited by
562
References
0
Language
en
Licence
creativecommons.org/licenses/by/3.0/ (vor)

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