Controlling Surface Roughness to Enhance or Degrade Image Appearance in Synthetic Aperture Radar (SAR)


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Posted: December 16, 2020 | By: DSIAC

SUMMARY

Surface roughness is one of the most important factors that contribute to the brightness or darkness of objects in a SAR image.  Surface roughness is the height difference in the surface’s imperfections.  Surface structure greatly influences the appearance of an object in SAR imagery.  A surface appears smooth if the height variations of the surface’s imperfections obey the Rayleigh or Fraunhofer criterion.  A smooth surface produces specular reflection that reflects more of the radar energy away from the radar.  A rough surface produces diffuse reflection that reflects more of the radar energy toward the radar [1].  Controlling the surface roughness of an object provides a means to enhance or degrade the appearance of objects in a SAR image.  This article proposes various methods be used to control surface structure that can have an immediate impact on the appearance of the images in SAR.

INTRODUCTION

SAR produces two-dimensional images of the mapped area.  An aircraft, satellite, or any other airborne vehicle with constant speed deploy a SAR.  A SAR interrogates the surface from a moving platform and receives the reflected signal back.  The aircraft’s or spacecraft’s motion and advanced signal processing techniques allow the SAR to simulate larger antennas.  The data collected at each location along the track of the SAR are received and then postprocessed together to produce a fine resolution image of the scene [2].  Factors such as the radar system’s frequency, polarization, and viewing geometry, along with surface characteristics such as land cover type, topography, and more, affect how much energy is scattered back to the radar.  The radar image’s brightness or darkness depends on the intensity of the radar energy returned.  The brightness features are a combination of a number of these factors.  Three main areas characterize the factors that fundamentally control the radar energy and target interaction.  These areas are the surface roughness of the target, the radar viewing and surface geometry relationship, and the moisture content and electrical properties of the target.  The radar viewing and surface geometry relationship largely depends on the SAR platform and its angle of approach to the target. 

Frequency and polarization of the radar are also inherent to the SAR.  Surface characteristics such as land cover type and topography, moisture content, and electrical properties of a target are also locally dependent.  These factors, for the most part, are hard to control over a target’s surface.  A “skin” of micro- and macro- structures provides the means to adjust the surface roughness based on the Fraunhofer and Rayleigh criteria.  Corner reflectors provide another way that allows reflection back toward the radar due to the double-ounce effect.  This causes the surface to look brighter.  The brightness or darkness of a radar image is heavily dependent on the surface structure [1].  This article focuses on how to adjust surface roughness to produce a desired effect on a radar image and its relationship to the local incidence angle.

FACTORS AFFECTING SURFACE APPEARANCE IN SAR

Several factors influence the appearance of SAR images.  These factors include wavelength or frequency of the incident waves, local incidence angle, look direction or aspect angle, corner reflections, and surface roughness.  The frequency of the incident radiation determines the penetration depth of the radiation.  Penetration depth tends to be longer for longer wavelengths.  Moisture content of a surface affects the penetration depth, as microwaves do not penetrate water more than a few millimeters.  Moisture content of a material also affects the dielectric constant of materials.  Dielectric constant consists of permittivity and conductivity, which are both dependent on the moisture content of the material.  A change in the moisture content provokes a significant change in the dielectric properties of the material.  An increase in the moisture content of a material also results in an increase in radar reflectivity [3]. 

Other factors that affect surface appearance in SAR include the local incidence angle and the look direction.  The local incidence angle describes the relationship between the viewing angle and the surface features.  This plays an important role in how the energy from the radar interacts with targets.  Figure 1 shows a schematic of the local incidence angle to a surface.  The surface slope, orientation, and shape play an important role in the interaction of the radar energy and the surface. Surface roughness is average height variations in the surface cover from a plane’s surface.

Figure 1: Local Incidence Angle and Geometry of the Surface (Adapted From Reference [4]).

If the height variations are much less than the radar wavelength, the surface appears smooth.  As the height variations become closer to the radar wavelengths, the surface appears rough.  Figure 2 shows a schematic of surface height variations.  A smooth surface causes specular reflection (annotated by “A” in Figure 3), while a rough surface causes a diffuse reflection (annotated by “B” in Figure 3) [1].

Figure 2: Surface Variations and Their Relation to Surface Roughness [1].
Figure 3: Surface Smoothness and Its Effect on Scattering [1].

The Rayleigh or Fraunhofer criterion describes the relationship that must be satisfied for a smooth surface.  Table 1 shows the Rayleigh and Fraunhofer criteria, where the height variation of the surface is ∆h,  λ is the wavelength of the incoming radiation, and θ is the incidence angle [5].

Table 1:  Rayleigh and Fraunhofer Criteria for Surface Smoothness [5]

A combination of these factors determines how the surface appears in a radar image.  Controlling the roughness of a surface can result in producing the desired radar return response from the surface.  The intensity of the radar return determines how bright or dark an object looks in a radar image.

The surface roughness can be measured using optical profilometry techniques.  A profilometer consists of a detector and a sample stage.  The detector determines the location of the points on the sample, and the sample stage holds the sample.  The probe or sample holder can move to get the required measurements.  An optical profilometer is an instrument that uses light to probe the surface features, such as surface roughness [6].

Figure 4 shows a schematic of a stylus profilometer in which the probe moves over the sample to measure the surface roughness.  A stylus profilometry uses force feedback and physically touching the surface to provide the surface roughness measurements.  Figure 5 shows a schematic of an interferometric profilometer (optical profilometry), which uses light instead of a physical probe.

Figure 4: Schematic of a Stylus Profilometer [6].
Figure 5: Schematic of an Interferometric Profilometer [6].

CONCLUSIONS

Image appearance in radar depends on a number of factors.  These factors include the frequency or wavelength of the radar waves, the incidence angle of the radar waves to the surface, and the look direction.  The three areas that have the most effect on the brightness or darkness of images in radar include the surface roughness of the target, the radar-viewing angle and surface geometry relationship, and the moisture content of the target.  Surface roughness is the height variations in the surface structure to a plane’s surface.  A surface appears smooth if it meets the Rayleigh or Fraunhofer criterion stated in Table 1.  A smooth surface produces specular reflection that reflects more of the radar energy away from the radar; thus, the object appears darker.  A rough surface produces diffuse reflection that reflects more of the radar energy toward the radar; thus, the object appears brighter [1].  A “skin” that consists of micro- and macrostructures linked tightly together and placed or draped on a surface will allow the manipulation of surface roughness.  Changing the surface roughness will result in changing the appearance of the target in radar images.

REFERENCES

[1] Government of Canada.  “Target Interaction and Image Appearance.”    https:// www.nrcan.gc.ca/maps-tools-publications/satellite-imagery-air-photos/remote-sensing-tutorials/microwave-remote-sensing/target-interaction-and-image-appearance/9311, accessed 8 June 2020.

[2] USGS.  “USGS EROS Archive – Radar – Synthetic Aperture Radar (SAR).” https://www.usgs.gov/centers/eros/science/usgs-eros-archive-radar-synthetic-aperture-radar-sar-processing-system?qt-science_center_objects=0#qt-science_center_objects, accessed 8 June 2020.

[3] ESA Earth Online. “Radar Course 2.” https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/ers/instruments/sar/applications/radar-courses/content-2/-/asset_publisher/qIBc6NYRXfnG/content/radar-course-2-parameters-affecting-radar-backscatter, accessed 8 June 2020.

[4] Rizzoli, P., and B. Brautigam.  “Radar Backscatter Modeling Based on Global TanDEM-X Mission Data.”  IEEE Transactions on Geoscience and Remote Sensing, https://www.researchgate.net/publication/259904538_Radar_ Backscatter_Modeling_Based_on_Global_TanDEM-X_Mission_Data, accessed 8 June 2020.

[5] Japan Association of Remote Sensing. “Surface Scattering.”  http://wtlab.iis.u-tokyo.ac.jp/wataru/lecture/rsgis/rsnote/cp3/cp3-4.htm, accessed 8 June 2020.

[6] Nanoscience Instruments. “Optical Profilometry.”  https://www.nanoscience.com/techniques/ optical-profilometry/, accessed 10 June 2020.

ACKNOWLEDGMENT

Permission to publish was granted by the Director, Geotechnical and Structures Laboratory, U.S. Army Engineer Research and Development Center (ERDC).

BIOGRAPHY

Qaisar Manzoor is a research physicist at the Geotechnical and Structures Laboratory at ERDC. His research focuses on understanding the interactions between electromagnetic radiation and material properties on synthetic aperture radar, infrared, and multispectral imaging. As a veteran of the U.S. Armed Forces, he served in the U.S. Navy, Army, and Army Reserve. Mr. Manzoor holds a B.S. in physics, with a second major in mathematics, from The University of Memphis. He is currently pursuing his master’s degree in applied physics from Johns Hopkins University.


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