Extraction of Linear Features using Synthetic Aperture Radar D. Buie Department of Applied Physics University of Sydney
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Abstract A very promising addition to the research armoury of archaeology is the use of airborne and spaceborne synthetic aperture radar. By exploiting the capacity of centimetre wavelengths to penetrate vegetation cover, a series of roads and irrigation channels hidden beneath the rainforests of Cambodia have been partially revealed. This paper applies and develops a segmented application of the Radon transformation, to isolate and correlate these linear features, and by doing so gives a reasonable to good approximation of the extent of infrastructure in Angkor; the former capital of the Khmer Empire. Keywords: Synthetic Aperture Radar, Radon Transformation, Segmentation, Image Processing, Angkor.
Introduction High resolution Synthetic Aperture Radar (SAR) and ground Digital Elevation Models (DEM) are introducing to archaeological research a new mode of data acquision. Radar, with its ability to penetrate vegetation cover, allows the monitoring of large tracts of land quickly and efficiently. For this study, portions of roads, remnants of walled structures and irrigation channels, normally concealed by tropical vegetation and rural activity, can be observed over broad areas of land beyond central Angkor. This not only offers us the ability to gain a global perspective of the area, but it has the ability to identify structures that would not normally be visible or readily accessible using more conventional archaeological means. By applying a segmented application of the Radon transformation to slices of preprocessed images, predictions as to the extent and relationship between varying isolated linear features and structures can be estimated. This paper focuses on the methodology used to extract these linearities from the SAR images in areas of heavily diffusive scattering and highlights the benefits of SAR to archaeological research. The area that we are specifically looking at is Angkor; the former capital of the Khmer Empire. It is situated about 15km north of the Tonle Sap, near the modern city of Siem Reap. Angkor plays an important role in analysing the limits of urban development (Fletcher R. J., 1995). To do this it is necessary to evaluate its approximate size and overall layout. Angkor was founded in the late 9th century and collapsed somewhere between the 15th and 17th centuries. Great temple structures mark the existence of the Khmer people, one of the grandest being Angkor Wat, built as a funerary monument for Suryavarman II in the first half of the 12th century. Along with the temple structures the city also had a vast infrastructure of roads, dykes and waterways. The greatest construction of these was undertaken by Jayavarman VII in the late 12th and early 13th centuries. He built a series of roads and irrigation channels, from which the size of this former capital can be defined. The area of specific interest is to the north of Angkor Thom. This area was completely covered by the spaceborne radar and was only partially covered to the south by JPL’s airborne radar (Image 1). The images for this research were collected by two methods. The first and largest data set was collected during the Space Shuttle Endeavour’s second SIR-C/X-SAR mission in October of 1994. Conducted as a joint co-operative between JPL (Jet Propulsion Laboratories), DARA (Deutsche Agentur fur Raumfahrtangelegenheiten) and ASI (Agenzia Spaziale Italiana), quad-pole data in both L-Band (23cm) and C-Band (6cm) wavelengths and a single
vertically sent and vertically received (vv) image in X-Band (3cm) was collected from an orbital altitude of 225km. An area of approximately 100km by 35km with a north-south orientation centred about Angkor Wat was acquired. The resolution of this mode of acquisition was 25m (12.5m per pixel). The second mode of data collection was JPL’s Airborne Synthetic Aperture Radar (AIRSAR). Two smaller data sets were collected running parallel in an east-west direction through the middle of the SIR-C/X-SAR images. The data was also collected in three wavelengths, quad-pole P-Band (63cm) and L-Band in conjunction with a vv CBand and also DEMs from an altitude of 8400m. The resolution of the AIRSAR data set is 10m per pixel and covers an area of approximately 12km by 60km.
Image 1: Diagram illustrates the area of interest and the coverage that has been obtained.
The real power of long wavelength imaging for archaeological applications is that it can propagate through the atmosphere with little interference, penetrate vegetation cover, and monitor large tracks of land. In hostile environments such as those in Cambodia, where antipersonnel mines litter the landscape, the imaging radar exposed linear features in our area of interest. Due to the complexity of the image scene, classical image processing methods failed to significantly increase the strength of these linearities. The Radon transformation applied in our study has been used in a number of applications but typically the research applications have dealt with the recognition of simple geometric features in highly structured two and three dimensional images. The images returned from this application of SAR are predominantly unstructured and therefore different approaches must be considered. This paper highlights these areas; it introduces the Radon or Hough Transformation, explains the processing techniques behind the algorithm and then illustrates some results.
The image processing was completed using combinations of AVS, Fortran and Matlab including its Image Processing Toolbox using VISLAB; Sydney University’s computer laboratory.
The Radon Transformation The application of the segmented approach to the Radon transformation, to isolate linear features, was derived from two separate areas. The first was a paper written by N. Fitton and S. Cox [2] discussing the automatic extraction of linear features from geoscientific data using the Hough transformation. The second was written by R. Ferron et. al. [3] where they were using the implementation of the Radon transformation to determine ship movements from preprocessed SAR images. The Radon transformation in a continuous Euclidean space is usually defined as
f (ρ,θ) ≡ ℜ ( f ) =
∫D ∫ f (x,y)σ ( ρ – x ⋅ cos θ – y ⋅ sin θ) dx dy
in which D represents the entire image domain, f(x,y) the function value at the position (x,y), delta is the Dirac delta function, rho the normal distance between the origin and the line and theta the angle between the normal and the x-axis. In the case of SAR imaging, the function f is just the grey value of a pixel at the position (x,y). Every linear feature in the image is mapped in the Radon space by integrating the amplitude of every straight line and placing this sum as a single point in the transform space. Colinear points in the image are expressed as a brighter pixel value in the Radon space. This process allows for two main benefits (Ferron R. et. al., 1996). The first is that the points in the image do not need to be concurrent for a definitive edge or line to be registered. The second is that the integration over lines not comprising any prominent structure, averages out the intensity fluctuations. This effect reduces the noise and interference characteristics and thereby strengthens the signal to noise ratio in the Radon space. Three further points should be noted concerning the Radon transformation. Peaks in the Radon space correlate with linear features in Euclidean space, so by thresholding and applying an inverse, the linear features are exposed. However, the inverse of the Radon transformation is boundless, so a peak in the Radon space will give a continuous line, the whole length of the image. This can be an advantage or a disadvantage depending on your application. Secondly, pixels in the centre of the image are weighted more heavily than those on the fringes of the image. This results from their proximity to other pixels. Thirdly, the Radon transformation is only effective on binary images.
Processing: SAR images are strongly dependent on the geometry, elevation and structure of the image scene. Along with the true ground response comes unwanted aspects of imaging, namely; thermal and speckle noise, heavy interference from vegetation and interference from the inhomogeneity of the target surface. The longest wavelength in the area of interest is 23cm which is not long enough to negate the effects of the jungle forests. These contributing factors make the isolation of linear features more difficult. Due to the complexity of the images, local statistical methods fail to map the truly inhomogenous nature of the ground response in the areas of heavy interference. This, combined with the fact that the linear features possess the same characteristics as the noise in the Fourier domain removes the classical approaches to image enhancement. The algorithm presented here removes the dependence on any preprocessing and actually uses the chaotic nature of the images to extract its features. This section identifies each of the modules in the flow diagram in Figure 1.
1. Preprocessing Although no preprocessing is necessary, it is still advantageous to increase the level of the signal to noise ratio of the true ground response. The jungle canopy acts as a wave plate, so by averaging over the three images in a waveband, will both increase the information contained in a single image and reduces some of the speckle effects.
2. Segmentation Segmentation refers to conducting the image processing on tiles or sub-images of the original. This ideally allows limits to be placed on the inverse of the Radon transformation. This was originally proposed by Mirmehdi et al. [5] and implemented by Fitton et al. [2]. A procedure termed by Fitton et al. as a,’ multi-resolution hierarchical scheme’ was also implemented. This allows the identification of features on different scales. Box sizes of 300 by 300, 100 by 100 and 50 by 50 proved most effective. Each time linear features are isolated, a pixel path of 5 pixels is set to zero to minimise the interference in successive passes.
3. Image Plains and Summation Due to the random nature of the scattering it was not possible to create any one binary image. To overcome this, slices were taken through the image according to pixel values, similar to contours on a map. Each of these slices were then summed in the Radon space. When looking for the Great North Road for an example, the structure of the road is present on all levels of the image. By assuming that there will be greater structure to the road segments than there will be for randomly scattered pixels in any one direction, then summing the values in theRadon space for each of the levels of the image, highlights those structures.
4. Edge Compensation and Local Maximum To compensate for the bias in the pixel position in the Radon transformation each pixel must be weighted. The process described by Fitton et al. proved to be effective. This process involves weighting each pixel in the images with a scaling factor allocated from a power of the inverse of the surface of maximum possible values, which in our case was a Radon transformed white box of comparable size. The top “n” percent of the image in the radon space was then transformed back to Euclidean space, where “n” is an input parameter. The process is then repeated with a different box size.
Discussion It is necessary to address some of the fundamental problems associated with this form of algorithm. Due to the variation in the structure of the lines, the subtlety of some of the linear features, and the size of the area of interest, it is necessary to conduct thresholding as percentages of local maximum and not as discrete values. This has two consequences; the first is that at least one line is to be drawn in each of the boxes regardless of any specific linear feature; and secondly, if there is one strong feature in a box then the more subtle features are ignored. Image 3 highlights these points. In the relatively structured image, there are obviously linear features that are not detected due to the presence of the strong diagonal road (a modern road - Route National 1). The weighting function to compensate the position of the pixel is effective on a broader scale but the identification of linear features is still somewhat dependent on the position of the boxes for processing. By varying the box size and altering their position it may be possible to reduce these effects. Although a box size of 300, 100 and 50 proved most effective it may be more appropriate to allow redundancy and allocate box sizes of 300, 150, and 100. By placing the premise of the detection of linear features on the random nature of the pixels, then the isolation of lines in two neighbouring boxes that are colinear is strong evidence of non-random features. As can be seen in image 4, the algorithm identified these features. The strong diagonal features are present in all of the box sizes. Image 4 also highlights one of the benefits of the Radon transformation in that the algorithm can be tuned to look in a particular direction only. In Angkor, where there was a strong dependency on north-south, east-west orientation, narrowing the looking direction takes some of the pressure off the compensation algorithm. Although the algorithm is automatic, one restriction is that it is expensive in terms of computer time.
Figure 1: Flow chart of the Algorithm to detect linear features in SAR images
Raw Images
Pre-Processing Image Plains Segmentation Vary the box size
Radon Transformation
Every Plain
Recombine Image Image Summation
Post-Processing
Edge Compensation Local Maximum
Comparison
Conclusion: For highly complex scenes where there seems to be little or no structure, classical image processing is a weak approach. As the noise and linear features possess the same characteristics in the Fourier domain and since local statistical methods fail to map the truly inhomogenous nature of the images, the ability to take a more classical approach is removed. By exploiting the complexity of the image scene, the application of a segmented approach to the Radon transformation over a number of slices provides reasonable to good results. The process that has been presented here is automatic in the sense that only the allocation of thresholding values and box sizes, need to be specified. These results suggest that this type of algorithm, where there is little preprocessing required, may form the basis of a tool to isolate and correlate linear features from heavily dispersive synthetic aperture radar.
Acknowledgements The work described here is ongoing research as a joint cooperative between both the University of Sydney’s Archaeology and Physics Departments, with the full cooperation and assistance of Ben Simons and the facilities and staff of Vislab; Sydney’s computer laboratory. None of the work could have been undertaken without the incredible service provided by Ellen O’Leary and the Jet Propulsion Laboratories. Also thanks should go out to the computer laboratory of Sydney University’s Geophysics Department for lending us their software licences.
Image 2: A reasonably structured image to the south of the Western Barray (L-Band (hv))
Image 3: Processed image of a similar area as image 2. Thresholding at 90%
Image 4 a & b: Processed unstructured image; left, orthogonal; right full Radon space. The central feature is the Great North Road).
References: [1] Fletcher R.,”The Limits of Urban Settlement: A Theoretical Outline”, Cambridge Press, 1995. [2] Fitton N.,Cox S.,”Linear Feature Extraction in Geoscientific Data.”, Australian Pattern Recognition Society, 1995. [3] Feron R., “Ship Wake Detection Using Radon Transforms of Filtered SAR Imagery.”. SPIE Vol. 2958, 1996. [4] Datcu M., Walessa M., “Maximum Entropy methods for despeckling and resampling Synthetic Aperture Radar images of rough terrain.” SPIE Vol. 3217, 1997 [5] Mirmehdi M., West G., Dowling G., “Label inspection using the Hough transformation on transputer networks” Microprocessors and Mircosystems, Vol. 15, pp167-173, April 1991.
