MAPPS/ASPRS 2006 Fall Conference November 6 – 10, 2006 * San Antonio, Texas

AN ELEVATION AND BACKSCATTER FEATURE EXTRACTION PARADIGM FOR POLARIMETRIC RADAR USING SPECTRAL CLASSIFIERS AND CANONICAL

UNITS

Verner Guthrie, Physical Scientist Edmundo Simental, Mathematician S. Bruce Blundell, Physical Scientist

U.S. Army Engineer Research and Development Center Topographic Engineering Center

7701 Telegraph Road Alexandria, VA 22315-3864

verner.n.guthrie@usace.army.mil simental@tec.army.mil

s.bruce.blundell@usace.army.mil

ABSTRACT Hyperspectral sensors are used to provide numerous bands of spectral data for image feature extraction. Many highly proven and effective paradigms have been developed exclusively for hyperspectral feature extraction. However, hyperspectral data lacks any elevation discriminant component, and elevation is usually very significant in feature separation. An immediate drawback of mixing hyperspectral and elevation data in one analysis is that the magnitude of the separate datasets is several orders different. Spectral sensors provide an abundance of information. However, some of the data are redundant and highly weather dependant. Radar sensors, however, and in particular, polarimetric radar, provide an abundant amount of information for image exploitation. In addition, the sensor is all weather, day/night independent, and does not have redundant bands. In this paper, the use of a spectral feature extraction paradigm is proposed, as well as spectral classifiers such as matched filter abundance, Mahalanobis distance, Euclidean distance or others, principal component analysis (PCA), polarimetric data, elevation data, and canonical units for feature extraction. Registered elevation and elevation-derived data are appended to a polarimetry radar dataset. The elevation data results in extra bands being added to a polarimetric dataset. This augmented dataset then becomes the input to a supervised classifier. The backscatter and elevation data are remapped to canonical units for PCA evaluation compatibility. The magnitude of all bands, including the elevation bands, is in the range of [0, 1]. The addition of the elevation bands to the PCA dataset allows for proper class separation in one analysis. Key Words: polarimetry, feature extraction, supervised classifier, canonical units, principal component analysis

INTRODUCTION

Remote sensing technologies have made possible the rapid generation of accurate and complete categorization, classification, and feature extraction maps. Classification and feature extraction are increasingly becoming available for a range of applications in several disciplines including geology, forestry, and environmental science. However, no single sensor is able to provide sufficient information to completely and accurately characterize complex terrain. One approach to increase the accuracy and completeness of this information is through the incorporation of information derived from multiple sensing sources. In recent years, multi-sensor remote sensing data has become the data of choice for classification (Serpico and Roli, 1997). Among the preferred data types for classification and feature extraction are radar backscatter and elevation data. These data types are increasingly becoming available from interferometric synthetic aperture radar (IFSAR) sources collected by one sensor, or separately by a SAR/LIDAR two-sensor combination. Classification without elevation data can sometimes lead to false results. For example, a classifier may correctly classify an asphalt parking lot and an asphalt rooftop in the same class (Simental, et al., 2003b). Without elevation data, the parking lot and rooftop will look indistinguishable in an image. With elevation-derived data, in this case, building height, separation of the rooftop and parking lot is trivial. This problem

MAPPS/ASPRS 2006 Fall Conference November 6 – 10, 2006 * San Antonio, Texas

also occurs in vegetation. Short three-foot bushes may appear to a spectral classifier as twenty-foot trees. The difference can be critically important in many military operations especially in mobility, trafficability, and line of sight assessments.

Previous work by the authors has focused on standard feature analysis techniques and the detection of micro-terrain features using radar datasets (Guthrie and Simental, 2003; Simental and Damron, 2001). Other authors have done research in the areas of forest classification (Ranson and Sun., 1994); geoscience applications (Elachi et al., 1990); and other applications (Vander Sanden and Ross, 2001) using polarimetric and synthetic aperture radar (SAR) data.

In this effort, the authors propose to demonstrate the enhanced remote sensing capabilities offered by the synergism of polarimetric SAR data with digital elevation data for accurate and complete terrain categorization (TERCAT), classification and feature extraction information. Therefore, the objective in this effort is to design, develop, test, and evaluate a paradigm for the efficient, seamless, and coherent classification and feature extraction of features in polarimetry backscatter imagery with an elevation discriminant. Results and conclusions are presented.

DATASET

The data used in this study is from the airborne synthetic aperture radar (AIRSAR) managed by the Jet Propulsion Laboratory, National Aeronautics and Space Administration (JPL/NASA) and was collected in a swath between Washington, D.C. and Baltimore, MD. The instrument is mounted on a modified NASA DC-8 aircraft. The AIRSAR is a side-looking radar instrument and collects fully polarimetric data (POLSAR) at three radar wavelengths: C-band (5.6 cm), L-band (24.5 cm), and P-band (68 cm) (JPL Website, 2006; Abundo et al., 1998). The polarization states for each frequency band are collected in two like-polarization states as horizontal transmit-horizontal receive (HH) and vertical transmit-vertical receive (VV), one cross-polarization state as vertical horizontal transmit-vertical receive (HV), and one combination state as total power (TP). Various digital reference datasets, including National Land Cover Data (NLCD) and United States Geological Survey (USGS) Digital Orthophoto Quadrangle Quarter-quad (DOQQ) color infrared images, were acquired over the study area and were georegistered to a common geographic datum and projection in the ESRI ArcMAP GIS environment. The AIRSAR polarimetry bands were then georegistered to the reference datasets in the ENVI image processing environment using control points obtained from the ArcMAP geodatabase.

The elevation data is a 10-meter digital elevation model (DEM) in GeoTIFF format obtained from USGS. This data was incorporated into the ArcMAP geodatabase and served as an additional layer or “band” of information to enhance the AIRSAR polarimetry dataset for feature extraction. During the georegistration process, the AIRSAR data was resampled to a 10-meter matrix to correspond to the USGS data, providing a one-to-one pixel correspondence between layers in the dataset.

A histogram of the elevation data is shown in Figure 1. This histogram is a sharp deviation from the Gaussian distribution expected, showing significant periodic spikes at 18, 24, 30, 36, and 42 meters. The probability of the natural occurrence of these elevation values occurring in such numbers and being so periodic (precisely 6.1 meters apart) is highly questionable. Therefore, it can be assumed that a systematic error has occurred. Analysis of the histogram combined with color mapping of elevation image pixels represented by the periodic spikes show that these suspected values, approximately 4.6% of the total elevation values, are spatially clustered in the elevation model. These clusters are shown in Figure 2, registered to a background elevation image of the study area. Table 1 below shows the number of pixels that fall into selected bins and the color names are indexed to Figure 2. The authors have removed these areas from the analysis. However, not all of the suspected values are in error and the preponderance of the values, 95.4%, are in agreement with other independent USGS map data. There is enough credible data for the valid analysis and conclusions for this effort.

MAPPS/ASPRS 2006 Fall Conference November 6 – 10, 2006 * San Antonio, Texas

Figure 1. Histogram of elevation data.

Table 1. Number of Pixels in Bins

Figure 2. Elevation data showing clusters of questionable elevations.

BIN VALUES (UPPER/LOWER LIMIT) NUMBER OF PIXELS COLOR 18.0 – 18.1 8962 RED 24.1 – 24.2 11463 ORANGE 30.2 – 30.3 10736 YELLOW 36.3 – 36.4 14617 GREEN 42.4 – 42.5 13676 CYAN

Total 59454

MAPPS/ASPRS 2006 Fall Conference November 6 – 10, 2006 * San Antonio, Texas

The study area is in the Odenton, MD vicinity. This suburb in the Washington, D.C. metropolitan area is characterized by urban structures (residential, commercial buildings and roads), some open fields and rural parkland. The imagery was flown in 1995. Road construction and other man-made features have added to the changes in the area since 1995. Figure 3 shows the study area as recorded by the AIRSAR sensor in the C band, HH polarization. Features include, but are not limited to, roads, fields, cropland, buildings, forest, bare ground, and powerlines. The elevation data varies between about 6 and 73 meters and depicts two broad valleys draining a region of dissected uplands (Figure 2). Previous studies by the authors examined the same dataset with an emphasis on terrain feature classification (Simental et al., 2004); polarimetry band ratios, decompositions and statistics (Simental et al, 2005); and denoising techniques (Simental and Guthrie, 2006).

Figure 3. Study site Near Odenton, MD, C-band HH.

METHODOLOGY

The main problem in integrating radar polarimetry backscatter and elevation data is that the unit dimensions of

backscatter and elevation data are not the same. We transform both datasets to canonical units to effectively use these two data types together.

Work in the scientific disciplines frequently involves operating with very large or very small numbers or datasets that are not user friendly. Canonical units result from a transformation of data to a more friendly and meaningful form. Canonical units, from trivial to complex transformations, have been prevalent in the scientific world for many years. Work in space science is one field that has broadly engaged these units (Sellers, 2000). An example of canonical unit conversion is the conversion of complex or uneven datasets to a ten-base logarithmic scale which is a common way to put numbers into a workable scale. Conversion of data to a two-base binary scale is also common in many scientific disciplines. Canonical units work as tools of conversion. Unlike classical units, like hours and seconds that measure time, and meters and feet, that measure distance, a canonical unit is not a measurement of anything in particular. It is a valued unit that converts or scales measurements to an easier, convenient, sensible, and workable form. There is no simple definition, ordinary or unique algorithm, or exact course of action for the conversion to canonical units. The conversion definition and conversion construction depends on the dataset and the purpose behind the conversion.

The conversion to canonical units in this effort is basically a conversion of data to a suitable form to facilitate analysis over a mixed dimensioned dataset. The dataset is composed of several radar backscatter polarimetry bands and one totally independent elevation band. The polarimetry bands are in reflectance, radiance, or intensity

MAPPS/ASPRS 2006 Fall Conference November 6 – 10, 2006 * San Antonio, Texas

brightness units, and the elevation band is in meters or feet. The classification paradigms are designed for spectral data and as long as the parameter dimensionality is consistent, mathematical operations are performed across the bands without any consequences. Once the elevation band is introduced in height units, this band will cause mathematical chaos and produce biased or totally bogus results. The elevation data are not only different in units, but it can be negative and numerically, it can be orders of magnitude different than the spectral data. Therefore, each band of the data is converted separately, including the elevation band, to dimensionless canonical units, where all the measurements are in the range of [0.0, 1.0]. This conversion prevents the occurrence of chaos in the classification, allows the working across bands without any consequences, and allows the inclusion of a height discriminant in the analysis for enhanced classification and feature extraction. The mathematical transformation to canonical units is shown below.

xt =xi − xmin

xmax − xmin

where xi is the ith value of the parameter to be transformed xt is the corresponding transformed value

xmin is the minimum band value, and xmax is the maximum band value

The form of the dataset is changed, but its classification significance is totally retained. Analogous to this in

some ways is the transformation of backscatter data from radiance to reflectance units where the numerical form is changed, but the data usefulness is unchanged. There are times when the conversions are performed at a risk of losing information, internal equivalence, data fidelity, or resolution reliability. This is especially true in data distributions that are non-Gaussian or distributions that have very long tails. However, in this effort, nothing is lost other than the small insignificant amount due to round-off and truncation in division and multiplication of numbers. The conversion provides excellent scaling properties, and as can be seen from the above expression, the conversion is simple, linear, and invertible.

Shown below in Figure 4 is a simplistic flow diagram of the methodology. One training set, and only one for comparison consistency, is constructed for the classification. There are many classification routines available and seven (7) were used: absolute difference, derivative difference, Euclidean distance, vector angle, K-means, Mahalanobis distance, and matched filter abundance. Classification routines are primarily, if not exclusively, designed for use with spectral data. Most of these routines can effectively be used with all-spatial or a combination of spatial and spectral data without much modification to the routines. The seven routines used comprise the classification array denoted as the A(k) array in the flowchart. Three (3) separate and distinct datasets from the polarimetric and the elevation data were constructed. The first dataset consists of canonical polarimetric backscatter and elevation data, the second of canonical polarimetric backscatter and non-canonical elevation data, and the third consists of canonical polarimetric backscatter with no elevation data. The array is denoted as the D(i) array in the flowchart. Each of the three datasets was classified with each of the seven classification routines and with and without principal component analysis (PCA). There are 42 classmaps to compare, analyze, and evaluate with each set of classification algorithm parameters. Every time a parameter is modified it generates another set of 42 classmaps. Not all analyses, comparisons, and evaluations are documented in this paper, but what is reported is a comprehensive representation of all the work conducted in this effort.

MAPPS/ASPRS 2006 Fall Conference November 6 – 10, 2006 * San Antonio, Texas

Figure 4. Methodology flow chart.

RESULTS

Image cubes (in the manner of hyperspectral cubes) were constructed with the polarized radar data (C, L, and P

bands, each with three polarizations and total power) and elevation files (USGS file) were added. The files were converted to canonical units and analyzed. An RGB composite of the HV, VV, and HH polarizations of the C-band was made which shows no degradation of the image after the files were converted to canonical units. Figure 5 shows the result.

NO

YES

NO

YES NO

Construct Classification Array A(k) k = 0, 6

Construct training set on image cube

Construct Data Array D(i) i = 0, 2

Enable PCA

Analysis, comparison, and evaluation: END

Generate and store classmap

Classification PCA enabled?

YES

Disable PCA i = i + 1 i = 3?

k= k + 1 k = 7? i = 0

MAPPS/ASPRS 2006 Fall Conference November 6 – 10, 2006 * San Antonio, Texas

Figure 5. RGB composite of C-band (HV, VV, HH).

One of the elevation files was converted to canonical units and another was not. A training set of twenty points was selected of an area in the image that is known to be an agricultural field. This training set was used in all the analyses. Classification routines were executed using the cube with either the elevation file disabled or enabled and some without using any elevation file in the analysis. Using the non-canonical elevation in the analysis results in a poor to useless classification since the elevation file dominates the scene. Man-made features do not appear. This is shown in Figure 6.

Figure 6. Classification result of K-means (Euclidean Distance) using non-canonical elevation.

The results of the classification are different when using the canonical elevation file. This is shown in Figure 7. Figure 7 shows man-made features (built-up areas) in orange and fields in green. The man-made features do not appear in Figure 6. Using the canonical elevation file yields better classification results.

MAPPS/ASPRS 2006 Fall Conference November 6 – 10, 2006 * San Antonio, Texas

Figure 7. Classification result of K-means (Euclidean Distance) using canonical elevation.

Other classification routines were used such as matched filter abundance. Figures 8, 9, and 10 show the results of the analysis. The matched filter abundance algorithm generates a gray shade output image that consists of projections of the source data against a constructed matched filter (in this case, points selected in a field).

Figure 8. Matched Filter Abundance without Figure 9. Matched Filter Abundance with elevation file. non-canonical elevation file.

Figure 10. Matched Filter Abundance with canonical elevation file.

MAPPS/ASPRS 2006 Fall Conference November 6 – 10, 2006 * San Antonio, Texas

The matched filter abundance result in Figure 10 shows more pronounced man-made features such as built-up areas and roads. A large agricultural field (upper right) shows little change among the three figures. Some open field features that appear in the two broad valleys on the left side of Figure 8 are less evident or do not appear at all in Figure 10. This may be caused by backscatter differences in the polarimetry bands that are large enough to force class separation, but which are reduced when the elevation band is added to the classification process due to similar elevations in the valleys where the field features are found. The apparent result is that some features in otherwise separable classes may coalesce into a smaller number of classes with the inclusion of the elevation band, causing them to be suppressed or to disappear entirely.

The elevation data are in the form of a digital elevation model. This data represents the ground elevations relative to a known datum and is void of any natural or man-made feature height information. Height information is a vital feature discriminant in classification and feature extraction. For example, two buildings with the same spectral/spatial characteristics, but one twenty feet taller than the other, would be assigned to the same class if no height information were used. The same is true of three-foot tall vegetation and a sixty-foot tree forest. Therefore, a simulated feature height image was generated in order to better assess the paradigm. Selecting known features classes from the polarimetry images, namely buildings and vegetation, formed the basis for the height image. The vegetation was further partitioned into three-foot vegetation and sixty-foot trees. Figure 11 represents the location and class of the assigned heights. The blue pixels represent buildings, the red pixels 3-foot vegetation, and the yellow 60-foot trees. The gray pixels have no height values.

Figure 11. Composite of height pixels.

The number of pixels that were assigned a height, approximately 52,429, is relatively small, which is about 4 percent of the total pixels in the image. It is, however, enough to illustrate the value of height discrimination in classification operations. Figure 12 is a representation of a classmap derived without feature height information and using a vector angle classification routine. The rectangle in the figure contains simulated 60-foot high vegetation. Note that this area in Figure 12 is indistinguishable from other vegetated areas above and to the left of the rectangle. Also note the anamorphous light green blob above and to the right of the rectangle. This area is a small, constant height, short vegetated field. The spectral/spatial characteristics of this area are slightly different that the higher vegetated area around it, but its class color is the same, or nearly the same as other fields.

Figure 13 represents a classmap of the same identical areas as in Figure 12. This classmap was derived using the same control points, same classification routine, and the same routine parameters. The only difference is the

MAPPS/ASPRS 2006 Fall Conference November 6 – 10, 2006 * San Antonio, Texas

addition of a feature height band. Note that the area inside the rectangle is substantially different than other areas. Also, the amorphous field in Figure 12 appears as a well-organized area in Figure 13.

Figure 12. Classmap derived without feature height information.

Figure 13. Classmap derived with height information.

Field

Field

MAPPS/ASPRS 2006 Fall Conference November 6 – 10, 2006 * San Antonio, Texas

CONCLUSIONS

Previous research has established that multi-band and multi-polarization radar interacts in detectable ways with phenomena that is strongly associated with natural topography and vegetation. More specifically, radar returns are associated with dielectric properties, density, size, geometric shape of features, and imaging geometry. These features respond differently to frequency and polarization. Prior research has also shown that radar can be used to characterize environmental conditions, surface terrain, and feature heights (Simental, et al., 2003a). These disciplines have not been integrated to synergistically map the terrain. Multi-band, multi-frequency, and polarimetry SAR in union with elevation/feature height data hold the potential to be employed as a powerful tool to improve terrain categorization. Multi-frequency, multi-polar radar has been used for mapping crop fields (Lombardo and Oliver, 2002), iced areas, and other features, but not specifically for terrain knowledge. Polarimetric and multi-frequency imagery is now becoming increasingly available; and as with other forms of SAR imagery, there are substantial gains to be made by integrating it with elevation and/or feature height data.

This research has shown that elevation and spatial/spectral data cannot be used concurrently in classification operations without scaling. It also shows that scaling all data to canonical units is a viable scaling method for classification which allows elevation or feature height information to be used in conjunction with spatial/spectral data for terrain categorization, feature extraction, and classmap generation. An elevation/feature-height component is also useful for feature separation when using polarized radar image data. Elevation dependent parameters, such as terrain slope, peaks, valleys, ridges, basins, contours, and drainage patterns can all be partially assessed with this process. The height discriminant adds an extra measure of accuracy in discriminating features because it can discriminate between trees, bushes, grass, and other foliage types that have the same spatial/spectral characteristic except for height. Where elevation change is minimal, however, some features may be suppressed in the feature classification process (Figures 8-10). When elevation data is useful, however, just using the elevation/feature height data without conversion to canonical units causes the elevation data to become the dominant criterion for classification and the spatial/spectral characteristics of features are enormously suppressed or totally lost and renders any classification results useless. The use of the canonical data file yields more complete results in that the canonical file can separate pixels that belong to the same spatial/spectral class but have different elevations. This information can be critical in mobility and trafficability operations.

ACKNOWLEDGEMENTS

This effort was implemented using MatLab and Interactive Data Language (IDL) commercial-off-the-shelf (COTS) software and HyperCube, an in-house software package. HyperCube is an application software program directed to the analysis and display of multi and hyperspectral imagery, developed by Robert Pazak of the U.S. Army Engineer Research and Development Center (ERDC), Topographic Engineering Center (TEC), Alexandria, Virginia (Pazak, 2006). The authors would like to appreciatively acknowledge the help of James Eicholtz of TEC who contributed valuable advice for this project. The authors would also like to thank Mary Brenke and Peggy Diego of the ERDC-TEC Imagery Office for assistance in obtaining data for this project. We would also like to acknowledge Rebecca Ragon and Melody Clanton for graphics support.

REFERENCES Abundo, R.V., E. Parringit, D.S. Domingo, and M. Lituanas (1998). AirSAR capability to geomorphology and

geologic mapping application. Proceedings of the Asian Conference on Remote Sensing (ACRS), Manila, Philippines, 1998.

Elachi, C., Y. Kuga, K. C. McDonald, K. Sarabandi, T. B. A. Senior, F. T. Ulaby, J. J. van Zyl, M. W. Whitt, and H. A. Zebker (1990). Radar Polarimetry for Geoscience Applications. Artech House, Inc.

Guthrie, V. and E. Simental, (2003). Detecting micro-terrain features using synthetic aperture radar. Proceedings, American Society of Photogrammetry and Remote Sensing Annual Meeting, Anchorage, AK-USA, May, 2003.

Jet Propulsion Laboratory Website (updated 2006). http://airsar.jpl.nasa.gov/index_detail.html

MAPPS/ASPRS 2006 Fall Conference November 6 – 10, 2006 * San Antonio, Texas

Lombardo, P., and C.J. Oliver (2002). Optimal polarimetric segmentation for the classification of agricultural areas. Proceedings, EUSAR 2002, Koeln, Germany, June 2002.

Pazak, R. (2006). HyperCube Users’ Manual. Topographic Engineering Center Internal Report, July 2006. Ranson, K.J., and G. Sun (1994). Northern forest classification using temporal multifrequency and multipolarimetric

SAR images. Remote Sensing of Environment, 47:142-153. Sellers, J. J. (2000). Understanding Space: An Introduction to Astronautics., McGraw-Hill Co., New York, NY-

USA, Appendix B, pp. 699-705. Serpico, B. S., and F. Roli (1997). Classification of multi-sensor remote sensing images by structured neural

networks. IEEE Transactions on Geoscience and Remote Sensing, 33:562-578. Simental, E., and J. Damron (2001). Analysis of SAR two pass data. Fifth International Airborne Remote Sensing

Conference, San Francisco, CA-USA, September 2001. Simental, E., T. Evans, R. Pazak, A. Krusinger, T. Hay, and V. Guthrie (2003a). Hyperspectral thermal, elevation,

and Mahalanobis distance for terrain classification. Presented at the ISSSR Conference, Santa Barbara, CA-USA, July 2003.

Simental, E., D. Ragsdale, E. Bosch, R. Dodge Jr., and R. Pazak(2003b). Hyperspectral dimension reduction and elevation data for supervised image classification. Proceedings, ASPRS Annual Conference, Anchorage, AK-USA, May 2003. Simental, E., V. Guthrie, and S.B. Blundell (2004). Terrain feature classification using multi-frequency polarimetric

radar. Poster presentation, ASPRS Fall Conference, Kansas City, MO-USA September 2004. Simental, E.,V.Guthrie,and S.B.Blundell (2005). Polarimetry band ratios, decompositions, and statistics for terrain

characterization. Proceedings, Pecora 16 Conference, ASPRS, Sioux Falls, SD-USA, October 2005. Simental, E., and V. Guthrie (2006). Curvelet techniques for analyzing radar polarimetric data. Proceedings, ASPRS

Annual Conference, Reno, NV-USA, May 2006. Vander Sanden, J. J., and S. G. Ross (2001). Applications of RADARSAT-2. Canada Centre for Remote Sensing

Preview Report, April 2001.