development by the Office of Naval Research (ONR) (under a small business ..... Features for Classification of Landsat TM Imagery of Central Colorado',.
Submitted to IEEE Transactions on Geoscience & Remote Sensing, IGARSS-2000 Special Issue Projection Pursuit Classification of Multi-band Polarimetric SAR Land Images Dennis Trizna, Charles Bachmann, Mark Sletten, Nick Allan Naval Research Laboratory, Code 7255 4555 Overlook Avenue Washington, DC 20375 Jakov Toporkov Sachs Freeman, Inc. Largo, MD Raymond Harris Metratek, Inc Fairfax, VA ABSTRACT Results are presented for an experiment utilizing a pastoral land scene with a variety of eight classes, imaged by the NRL dual band (X and L) polarimetric synthetic aperture radar (NUWSAR) at a spatial resolution of 1.2m. Projection Pursuit statistical analysis tools were applied to a set of simultaneous L and X-band fully polarized images to demonstrate the utility of land classification at high spatial resolution from a light aircraft using synthetic aperture radar. The statistical confusion matrix was used as a quantitative optimization measure of classification. Samples of eight classes from a portion of the scene were used to define a training set, then Projection Pursuit tools were used for classification. It is clear that L-band and X-band fully polarized data view the classes in a significantly different manner, and each brings independent information to the analysis. Comparisons were also made with just the L-band data for comparison. These results are not meant to be exhaustive at this time, but to demonstrate the utility of applying projection pursuit tools to multi-band and polarization SAR data, and to give an indication of the quality of classification one can achieve with moderately high spatial resolution SAR data using a light plane platform. INTRODUCTION The Naval UltraWideband Synthetic Aperture Radar (NUWSAR) is a new radar system under development by the Office of Naval Research (ONR) (under a small business administration research contract with Metratek, Inc) and the Naval Research laboratory, for ocean and land remote sensing research. Several features characterize its operation: (1) pulse-to-pulse multi-channel capability, allowing interleaved combinations of radar frequency and polarization; (2) small package, deployment capability on either a P3, light aircraft, or UAV; (3) 1.2 to sub-meter spatial resolution; (4) greater than 40 MByte/s data recording speed using RAID arrays; and (5) full motion compensation using a Litton LN100G INS system and/or an Ashtech differential GPS. The NUWSAR antenna system layout for both cross-track and along-track interferometry on a Piper Navaho aircraft is shown in Figure 1. One dual polarized L-band antenna (upper left) and a set of three 3-15 GHz dual polarized antennas is shown, mounted in the doorway behind a microwave transparent fiberglass door replacement. For higher frequencies, the two lower antennas are used for along-track interferometric data; the upper right antenna is use for cross-track INSAR data, and the lower right
antenna is used for transmission for all cases. We discuss here polarimetric data collected with both X and L bands, switched pulse-to-pulse, so that all images are collected simultaneously, simplifying motion compensation processing. EXPERIMENT DESCRIPTION The data used for the analysis consisted of three polarized channels for both L and X band. Parameters for the collection are shown in the table below. TABLE 1 – COLLECTION PARAMETERS Radar Parameters Frequency Polarization PRF (Hz) Bandwidth (MHz) Antenna 3-dB BmWd(deg) Altitude (ft) Range Samples Aricraft Speed (m/s) Doppler Ambiguity (Hz)
L-band 1.385 HH/HV/VV 1,667 125 45 1,000 1,500 60-80 942.8
X-band 9.75 HH/HV/VV 1,667 125 11 1,000 1,500 60-80 1017.6
Calibration Data Images collected for polarization calibration runs are shown in Figure 2, L-band at top and X-band below. The data are displayed in RGB format images (Red = HH, Green = HV, and Blue = VV) of the Warrenton-Fauquier, VA, airport area. Only 500 range bins are used for this collection, vs. 1500 for the classification data. All data presented here were collected at a speed near 60 m/s, with temporal variations accounted for by resampling the data matrix in both range delay and pulse number, adjusting the complex phase according to the motion compensation and velocity data. Single-look integration times of 1-s and 4-s were used for X- and L-band data, resulting in single look cross-track resolution of 0.11 and 0.22 m for X and L-band, respectively. A chirp scale SAR processor was used for the analysis, and an azimuth-compressed range line was calculated stepping through the azimuthal time series one pulse at a time. Then 11 looks were averaged for X-band, again stepping through the data one pulse at a time to maintain the same number of azimuth samples. Then 11 of these samples were averaged to form an azimuthal resolution of 1.2 m, the same as the range resolution. For the L-band data, 6 looks were averaged stepping through the data in similar fashion, with additional averaging of these to provide the same azimuthal resolution as for X band. Pixels are therefore nearly square. The red vertical line at about 40% though the scene in Figure 2 is due to the display software and does not represent data. The calibration targets are shown in the lower mid-center of the images, in the dark ellipse region of the X-band image, which was a flat cleared field area. There are six bright calibration targets that are apparent, with the right-most one being a 45o rotated dihedral showing as green, pure HV polarization, as it should. The runway has the lowest reflectivity of any part of the scene, but shows clearer at X-band as the adjacent area echo was stronger at X than L.. The horizontal streak along the lowest portion of the image are metal airplane hangars that provide an exceedingly strong echo that has range sidelobes in the 1500 sample data and provide some problems with the classification analysis later. The second horizontal line above the hangars is a line of pine trees, which cast a shadow directly above. The outline of the treetops can be seen in the shadow. The effective illumination grazing angle is about 10o for this
and following data, which causes the strong shadowing. A number of aircraft can be seen and discriminated standing in the grass between the hangars and the tree line. The inverted V-shape line of dots about a third from the left of the image is due to metal roadside crash barriers. The metal posts holding the horizontal barriers shows strongly, while the long road-side horizontal barriers themselves reflect specularly away from the radar. The texture of the forested area shows remarkably well at 1-m resolution. On the right-most portion of the L-band image below the forest area is a curved line running downward at 45o that represents a creek, showing far stronger at L-band that at X, mostly due to the weaker surrounding clutter from the field area for L band. The very strong echoes and range sidelobes just to the left of center are due to buildings associated with a plant nearby. Classification data Figure 3 shows a set of data collected for classification analysis, 1500 samples in range this time, again at 1.2 x 1.2-m resolution with L-band at top and X-band below. The range sidelobes show up as vertical wispy bands below the hangars, and these will show up in the classification analysis, as we shall see. The airport area is seen at the top of the image, and the range sidelobes are quite strong, at X-band in particular. These occur as a result of the flights being parallel to the corrugated metal wall airplane hangars, which form a strong dihedral scatterer. The 3-dB beamwidth of the X-band antennas is roughly 11o at X-band, and the first two sidelobes of the antenna pattern show as dark streaks in the lower part of the image, Their variation in the range dimension (upward) with time running left to right is indicative of the aircraft roll motion, for which the motion compensation accounts. These sidelobes forced the excising of the lower portion of each image for classification analysis In addition to the weakening signal strength in the lower portion of the X-band image, the polarization properties of the antennas change as well, brown changing to blue in the same field area, making the classification analysis invalid there using X-band data. This region was therefore excised from the data for classification analysis. These images show a number of quite distinct differences for X and L-band data. The tilled fields can be seen in the three field areas at lower left in lighter blue at L-band, and the furrows can be seen running left to right in the detail. The X-band results for the same area are not distinguishable and show similar amount of HH and VV scatter, while the L-band data are dominated by VV. This suggests that this tilled agricultural soil surface satisfies the small scattering perturbation approximation at L-band (HH>>VV), with roughness scales of order ten cm, while not so for X-band. Note that the deciduous trees show strongly in green at L-band, suggesting scatter from twig structures below the leafy canopy at position angles other than vertical and horizontal. For X-band, the deciduous trees are not dominated by any particular polarization, suggestive of leaf scatter. The beaten down corn stalk fields show up as red for both polarizations, as the stalks are nearly horizontal. The darkest regions in blue show only a weak VV echo in the L-band, while polarizations are all similar in amplitude in X-band These are cut hayfields or grass areas with little roughness at L-band wavelength scales. The uncut hayfield shows greener (strong HV echo) than cut fields with moderate amplitude at L-band, suggesting grass stalks bent near 45o as dominant scatterers; at X-band the polarization property is little different than cut grass areas, and slightly weaker in amplitude. The cucumber shaped area bent at 45o centered in the trees to the left of the airport area is a lake, and will be confused in the classification with weak asphalt echoes, due to its weak radar cross section for all polarizations and both frequency bands. The key for the classification was manually defined for determination of the ‘confusion matrix’ to be discussed later, and is shown in Figure 4. The classes used for the analysis were the following: (1) ‘Asphalt;’- roads and runways; (2) ‘Corn’ - dried beaten down stalks sitting in the field after harvest; (3)
‘Fences, Shrubs, and Small Trees’ – which were intermingled at edges of producing fields; (4) ‘Tree’ – heavy covered forested area, consisting of deciduous woods bordered by pines, as well as pine stands similar to that in Figure 2 above the hangars; (5) ‘Hayfield’ – grass land uncut; (6) ‘Tilled Earth’ – notable in the L-band data particularly, where the furrows could be distinguished; (7) ‘Grass’ – cut grassy areas, between the runways, for example, as well as the cut hayfields; (8) ‘Buildings/Development’ – all man made structures. Projection Pursuit Methodologies Projection Pursuit (PP) methods are powerful techniques for extracting statistically significant features from remote sensing data for automatic target detection and classification. Remote sensing applications, especially those related to the analysis of imagery, are typically characterized by a very high number of effective dimensions. PP techniques automatically determine the low-dimensional projections of such data sets that best highlight any inherent structure or clustering, which can then be used to detect and classify the targets or clutter associated with it. The details of the methods used are published elsewhere, and will not be discussed here (Bachmann, et all – submitted manuscript). Relating it to another popular image analysis method, Principal Component Analysis represents one member or subset of this more general PP family (Bachmann & Donato, 1999). Previous applications of PP methods have been used in remote sensing with optical and IR hyperspectral image data, which has far more channels than considered here (Jiminez & Landgrebe, 1999). The results of applying PP analysis to the six image planes (Lhh, Lhv, Lvv, Xhh, Xhv, Xvv,) for single pixel analysis that does not account for neighboring pixel statistics is shown in Figure 5. Somewhat different colors have been used than for the key in the previous figure. The effects of the range sidelobes due to the airport hangars is seen to be strong and confuses the cut hay fields area with corn. The effects of the change in polarization at the lower portion of the X-band image pointed out earlier still has an effect on the classification with this excised scene– it does not allow the tilled fields to be distinguished from the adjacent grass fields when including the X-band data. Figure 6 shows the ‘confusion matrix’ – plot of what the classifier chose for a pixel ID compared to the classification key. A perfect score would have all of the diagonal elements scored as 100% and all off diagonal elements as 0%. Additionally, totals along the Ground Truth line always add to 100% (sum of percentages from left-front to back-right). The worst case performance, for example, is the uncut Hayfield category, since more than 5 times as many pixels of tilled earth are being identified as Hayfield. Alternatively, Tilled Earth has the highest success rate of classification. The large number of Tilled Earth scores (left-back to right-front scan) indicates that other categories are being mislabeled as Tilled Earth, Hayfield being the worst case and grass the second. The success of classification in order is then: Tilled Earth, Corn, Asphalt, Grass, Tree, Fence, Buildings, and Hayfield. Regarding misclassification, for the Asphalt class (left-front to back-right), there is some confusion with trees and grass, for example, along the tree shaded areas, which were left unclassified in the Key (white areas). Thus, the classifier has succeeded in finding pixels that satisfy the low signal level criterion for all image planes, and the error is really one in establishing the key correctly and in the classification. A similar statement can be made about the small lake area One of the worst performers is Buildings/Development, where Corn, Fences, Tilled Earth and Grasses all were chosen instead. Again, this is due to the key simply placing a box around the area of development, which contained pixels that
satisfied the criteria for each of the ‘mislabeled’ classes, and reflects an error in the key and not the classifier. In looking at the image classifier results of Figure 5, one sees a certain amount of variation in the texture of the results. This may be due to unremoved speckle in the SAR image, or simply reflect the spatial texture characteristics of the data. To this end an analysis was performed using a 13 by 13 pixel box for analysis, in which the adjacent pixel statistics were accounted for. The resulting classifier image result is shown in Figure 7. It shows a thickening in what were narrow image features, due to the boxcar analysis, but does for example identify the location of man-made structures quite reliably in all cases. The corresponding confusion matrix is shown in Figure 8. It shows an improvement in performance for what previously were low percentage successes, including the Building/Development class. This is at the expense of poorer results for the higher percentage successes previously. Nonetheless, for improvement in classification of those classes that occupy small portions of the scene, such as Building/Development, a misclassification of the larger areas is a small price to pay. Finally, in an attempt to minimize the effects that still remain of the poor X-band antenna properties in the lower half of the portion of the image used, we used just the 3 polarizations of the L-band data. Figure 9 shows that result. There are still vestiges of range sidelobe effects that occur at L-band, which can be faintly seen in Figure 3, and the affected area is still mis-classified as the adjacent corn field. The Tilled Earth area shows up much better now due to the L-band data alone, as the area is apparent to the eye in Figure 3. However, this comes at the expense of a poorer job of classifying the asphalt areas due to poorer contrast with surrounding grass. Similarly, all tree-shadowed areas are mis-classified now, compared to the six-plane results, due to the higher contrast of the shadows at X band in that case. This suggests that the L-band signal does penetrate the tree tops and return some energy, since the shadowed areas do appear affected in that region, but not as strongly as at X-band. SUMMARY We have applied Projection Pursuit analysis to six-image L and X band intensity-polarimetric (not including phase) SAR data, using only the RCS statistical characteristics across the six image planes. We do not consider phase relationships, as in true polarimetry, as the off-axis phase quality of the antennas did not warrant it. Six classes were identified for the classification, but there is no limit in principle to the method used. These consisted of (1) asphalt roads and runways; (2) corn fields after harvest; (3) field boundary items; including fences, shrubs, and small trees; (4) tree stands or heavily wooded areas; (5) uncut hayfield; (6) tilled earth with apparent furrows seen in L-band data; (7) short grass areas, surrounding airport runways, for example; and (8) man-made structures, including buildings, automobiles, etc. The first classification experiment used just single pixels across the six image planes, and did not account for spatial texture in the data. We found that strong echoes from the metal airport hangars caused strong range sidelobes that affected the classification quality in such areas. Nevertheless, the classifier did remarkably well for all six classes. In many cases, the disagreement with the classification key was the result of generalizing areas, such as buildings and developed areas being designated as a single area, while the classifier found grass and asphalt in the area. Secondly, adding texture to the analysis by including the statistical properties in each band across a 13 by 13 matrix improved the classifier’s ability to find areas with a small fractional area coverage more
accurately and improved the statistics in the performance rank given by the confusion matrix. Finally, because of stronger range sidelobes due to the strong airplane hangar buildings at X-band, an attempt was made to use just the three L-band image planes for classification. This resulted in some improvement in the areas affected by the range sidelobes, but resulted in a poorer job of identifying asphalt and other low cross section areas, which were confused instead with cut grass. The latter had a low cross section at Lband, as the roughness scales are much than an L-band wavelength, but showed a strong response at Xband for vertical polarization. . These preliminary results demonstrate the utility of using just 2 radar bands with full polarization in classifying a pastoral scene at 1.2-m spatial resolution. This is the first time such a radar system was flown on a light aircraft, and shows the utility of such a system for both civilian and military applications. Adding radar additional bands and multi-spectral or hyperspectral bands should improve the classification even more so, and do not add to the complexity of the system to any degree. FIGURE CAPTIONS Figure 1. Polarimetric L-band antenna (upper left) and X-band antennas for along-track and cross-track interferometry are shown, with LN100G INS mounted below, on Piper Navajo aircraft. Figure 2. Calibration SAR imagery of Warrenton VA Airport in is shown for L and X-band polarimetric in RGB format (HH/HV/VV) at 1.2-m resolution; calibration targets lie in the ellipse in the image center. Note the L-band green tree color, indication strong HV relative power, suggesting double bounce scatter from tree limbs and penetration of the leaf foliage. Figure 3. Classification SAR imagery as before, but with 1500 range samples and a longer azimuthal extent. The WF airport can be seen in the upper right of each image, leading to strong sidelobes. Figure 4. Classification key for the upper portion of the images that were analyzed, for the eight classes used, as defined in the text.: Figure 5. Classification result using single pixels across six information planes for two radar frequencies, three polarizations each. Figure 6. Performance level of previous figure classification, expresses as a ‘confusion matrix’ - % of pixels labeled vs. classification key. Figure 7. Classification, as in Figure 5, but using texture statistics in 13 by 13 pixel box. Low percentage classes are better detected now, such as Buildings/Development, at the expense of large area classes. Figure 8. Confusion matrix for results of Figure 7, showing improved classification of low percentage coverage classes. Figure 9. Classification, as in Figures 5 and 7, but for just 3 L-band polarizations. Improved Tilled Land classification results, but poorer for Asphalt and other classes.
REFERENCES 1. Bachmann, C. M. and T. F. Donato, 1999. Mixtures of Projection Pursuit Models: An Automated Approach to Land-Cover Classification in Landsat Thematic Mapper Imagery. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS99’), Hamburg, Germany, June 28-July 2, 1999, pp. 339-341. 2. C. M. Bachmann, T. F. Donato, R. A. Fusina, and O. Weatherbee, “A Methodology for Automated Land-Cover Classification in Multi-Sensor Imagery of Coastal Environments,” submitted to IEEE Trans. Geoscience and Remote Sensing. 3. Bachmann, C. M., T. F. Donato, R. A. Fusina, 2000. Automated Classification of coastal wetland environments from multi-sensor imagery using projection pursuit methods. INTECOL 2000 Millennium Wetland Event (International Association of Ecology 6th International Wetland Symposium/ Society of Wetland Scientists 21st Annual Meeting), August 6-12, 2000, Quebec, Canada, Program with Abstracts, pg. 410. 4. Jimenez, Luis, and David Landgrebe, "Hyperspectral Data Analysis and Feature Reduction Via Projection Pursuit, "IEEE Transactions on Geoscience and Remote Sensing. Vol. 37, No. 6, pp. 2653-2667, November, 1999. 5. Bachmann C. M. and Donato, T. F., ‘An Information Theoretic Comparison of Projection Pursuit and Principle Component Features for Classification of Landsat TM Imagery of Central Colorado’, International Journal of Remote Sensing, Vol. 21, No.15, pp. 2927-2935, 2000 .
