LAND COVER/USE MAPPING WITH QUAD POLARIZATION RADAR AND DERIVED TEXTURE MEASURES INTRODUCTION

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1 LAND COVER/USE MAPPING WITH QUAD POLARIZATION RADAR AND DERIVED TEXTURE MEASURES Terry Idol Barry Haack Salim Sawaya Arjun Sheoran George Mason University Department of Geography MSN 1E2 Fairfax, VA , USA ABSTRACT The increasing availability of quad-polarization spaceborne radar provides new opportunities for the remote sensing community. Historically, most spaceborne radars were single wavelength and single polarization. The Japanese PALSAR system collects quad-polarization data as does the recently launched RADARSAT-2. The purpose of this study was to examine spaceborne quad-polarization radar data for Washington DC. For this study, the Japanese ALOS PALSAR data were obtained at 12.5 m spatial resolution. Previous studies by the authors had indicated the increased value of using measures of texture in addition to the original radar values. The Variance measure of texture was obtained at different window sizes for each radar band. The basic approach of this analysis was to evaluate the PALSAR radar, and radar texture, for land cover classification. Four land covers were identified and the ability to classify them assessed by use of the Transformed Divergence (TD) separability measures. TD values were obtained for all original and texture derived bands along with various multiple band combinations. The radar texture bands greatly improved upon the TD values in comparison to the original radar values. The selection of best bands from a combination of radar and radar texture typically identified a mix of original radar and radar texture bands. The radar texture was particularly useful for delineating urban areas. Keywords: radar, texture, separability, land cover, Quad-polarization, Washington DC INTRODUCTION Geospatial science and technologies are applied to a myriad of problems and tasks across the private and public sectors that affect each of us on a daily basis. The capabilities of these technologies are highly dependent, and often constrained by, the availability, timeliness, and accuracy of the datasets for the problem at hand. The growth in the usage of these technologies for issues related to land cover/use has led to an increasing demand for current, accurate spatial information to assess problems and tasks such as environmental studies, economic planning, resource management, disaster preparedness and response, and homeland security in many diverse and separate geographical regions. Basic information concerning land cover/use is critical to both scientific analysis and decision-making activities in a variety of businesses and disciplines. Significant challenges and costs are associated with the collection of these datasets which require the most efficient and effective systems to be used for these purposes. An important and highly useful approach to the collection of current and reliable land surface information is spaceborne remote sensing. Traditionally, this has taken the form of multispectral systems, such as the Landsat Thematic Mapper (TM). Multispectral sensors collect reflectance data in the visible and near-infrared wavelengths, which are highly effective for the identification of vegetated land covers. However, the inability of optical sensors such as LANDSAT to differentiate land covers with similar reflectance characteristics poses a problem. More recently Earth observation research has grown with the launch of several satellite systems capable of collecting radar data. Whereas optical sensors are sensitive to surface reflectance of the shorter wavelengths in the visible and infrared range that constitute the sun s energy, radar is an active sensor that emits and detects wavelengths that are

2 significantly longer. These longer wavelengths of radar are not hindered by atmospheric conditions, such as clouds, that limit traditional spaceborne optical and multispectral systems (Henderson et al., 2002). Furthermore the active radar sensor is not dependent on the sun to create surface reflectance and can therefore operate at night. These operational characteristics hold enormous data-collecting potential for many geographic areas around the world, especially those often obscured by adverse weather conditions. The surface interaction of radar is also very different than optical data, providing unique information that traditional sensors do not. Due to the active nature of radar, whether or not the sensor detects any surface response is a function of incident angles, landscape geometry, material dielectic constant, and surface roughness, which collectively constitute the amount of energy returned to the sensor (i.e. backscatter). For example, flat surfaces act specularly and reflect energy to continue traveling away from the sensor, while angular and hard surfaces such as buildings often reflect energy back to the sensor. Therefore, the response of radar is a function of surface roughness, geometry, and internal structure unlike optical data which is a function of which wavelengths of energy the surface of the earth reflects back to the sensor There are multiple challenges to the application of radar data and geospatial technologies to scientific and analytical problems. The different characteristics of radar data must be understood by the user to effectively and accurately draw conclusions from these analyses. Furthermore, the use of radar data in these applications is constrained due to the limited amount of data that is collected by most current radar sensors. Up until recently, most radar spaceborne systems only collected data using a single wavelength with a fixed polarization. Therefore, only one component of the total surface scattering is being measured, while any additional information contained within the returned radar signal is lost (Toyra et al., 2001; Dell Acqua et al., 2003). More recent systems, such as the Japanese PALSAR and the Canadian RADARSAT-2, use an increased number of polarizations which will potentially provide an immense amount of land cover/use information for a larger area (Banner and Ahern, 1995; Hegarat-Mascle et al., 1997; Gauthier et al., 1998). The information contained in remote sensing data is a feature of both the brightness value for each pixel (spectral) and the spatial arrangement of the pixels, which can be captured in textural information extracted from the pixels (Anys and He, 1995; Kurosu et al., 1999). Traditional image classification methodologies extract information, such as land cover/use classes, from the raw data based purely on spectral characteristics. Such an approach presents challenges to the accurate classification of land use classes, such as residential or urban areas, that are more easily distinguished by their spatial characteristics (Solberg and Anil, 1997; Townsend, 2002). The advantages of using derived radar measures, such as texture measures at different window sizes, in comparison to original radar data has been demonstrated by Haack et al. (2002) and Herold et al. (2005). The intent of this study was to examine the utility of the original radar data and derived texture measures for quad-polarization imagery (HH, HV, VH &VV). A Japanese Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) quad polarization spaceborne radar image collected on April 17, 2007 at 12.5 m spatial resolution was obtained for Washington DC. The primary methodology to evaluate this image was Transformed Divergence measures of separability. Successful results from this study can, hopefully, be extended to other radar datasets, locations, and applications. THE STUDY AREA AND DATA The PALSAR image obtained for this study covers Washington, D.C. and much of the surrounding counties of Fairfax, Virginia; Prince William, Virginia; Prince Georges, Maryland; Charles, Maryland; and Montgomery, Maryland. Several cities, including Bethesda and Silver Spring, Maryland, and Annandale and Arlington, Virginia are also located on the image. Figure 1 is the PALSAR imagery obtained for this study. The Washington, DC metropolitan area is a highly developed urban and suburban landscape that also contains water features and forest areas of significant sizes. In this scene, the vast majority of high backscatter areas, light tones, are the urban features in and around Washington, D.C. Built up suburban residential areas are present across much of the scene and demonstrate a mix of high and low radar returns. land cover is also present in the PALSAR scene and typically displays a low, although mixed, radar return. The dark linear feature generated by low radar return values that runs through the image is the Potomac River. Several of the major roads are clearly visible as dark specular lines. The land cover/use classes selected for this study were built up urban (or, simply urban ), residential, forest, and water.

3 Figure 1. PALSAR scene of Washington, D.C. collected on April 17, Approximate image width is 30 km, and length 60 km. North is at the top of the image. Copyright JAXA ALOS PALSAR. The area of interest (AOI) for urban cover is centered on downtown Washington, D.C. The residential AOI selected is located in Temple Hills, Prince George s County, Maryland. The forest land cover AOI is located in a large forested area approximately 5.0 kms north of Waldorf, Maryland. The AOI for the water land cover is located in the Potomac River, between Hog Island, Virginia and Indian Queen Bluff, Maryland (see Figure 2). Urban Area Area Figure 2. Area of Interest in PALSAR scene of Washington, D.C. Copyright JAXA ALOS PALSAR.

4 The PALSAR data were in a compressed and zipped format provided by the Alaska Satellite Facility (ASF) at the Geophysical Institute of the University of Alaska Fairbanks. ASF provided a downloadable software package named CONVERT to convert to an ERDAS IMAGINE compatible format. Full documentation was on line for the CONVERT tool. Recently, the ASF replaced the CONVERT tool with the MapReady Remote Sensing software package. The PALSAR data were converted from their raw format to four 32-bit floating point GeoTIFF files (one for each polarization). To process the radar image most effectively and efficiently, the four image files were rescaled from 32-bit floating point to unsigned 8-bit using the Interpreter > Utilities > Rescale module within the ERDAS Imagine software. Examining the mean and standard deviation of spectral signatures for different land cover/use categories can provide unique and validating information about the radar image. Spectral signatures for the four areas of interest (AOIs) were extracted and the results are presented in Table 1. The AOI sites varied in size from 4.36 sq kms for the forested area, 10.5 sq kms for water, sq kms for residential, and sq kms for urban. Table 1. Selected DN mean and standard deviations by land cover/use and PALSAR data layer (mean/standard deviation) Land Cover/Use Type Band HH HV VH VV Urban The results for the urban and water are very much as were expected (i.e. highly separable). The mean backscatter values for all the bands are the highest for the urban area. This urban area also experiences the highest standard deviation for the return values. provides both very low returns and low standard deviations. The forest and residential areas have very similar return values across all four polarizations. For three bands, the forest backscatter values are slightly higher. For the other band, the residential area has slightly higher backscatter values. However, the residential area has a consistently higher standard deviation value compared to the forested area across all four polarizations. There is a pattern of the mean digital number (DN) values and standard deviations of the mean DNs across the four bands. The VV and HH (bands 1 and 4) show much higher values for all four land cover/uses when compared to the HV and VH (bands 2 and 3). Band 1 (HH) has substantially higher values than band 4 (VV) for the areas of urban, residential, and forest, but slightly lower values for water. Bands 2 and 3 (HV and VH) have very close DN and standard deviation values for all four features. METHODOLOGY The first step in the methodology was to extract Variance texture measures at three different window sizes for each band of the PALSAR data. The window sizes were 5x5, 7x7, and 11x11. Areas of interest (AOI) for the four primary land covers/uses were then identified and spectral signatures were extracted from both the original and the texture data. Transformed Divergence (TD) separability measures were then calculated to evaluate the relative value of the different bands and texture measures for feature classification. Transformed Divergence, which is calculated from the means and covariance matrices of each spectral class or calibration site, is a measure of the statistical distance between class or site pairs of interest and provides information on their separability. This separability is an indirect estimate of the likelihood of correct classification between different data sets or derived measures (Swain and Davis, 1978). Such an estimate reduces the time consuming and expensive process of land cover/use classification and accuracy evaluations. A TD value of 1500 or greater generally indicates an acceptable separability between classes (Latty and Hoffer, 1980). The TD separability level

5 does vary as a function of the complexity of the input data. It is lower for simpler data sets and higher for more complex data. The maximum or saturated TD value is One representative calibration site for each of four classes was selected for the TD calculations. This was to avoid intra-class TD comparisons and their influences on the TD values. The sites include the four land covers/uses of urban core, residential, forest and water. These four features were considered sufficient for this initial study. In the future, a more complex classification system could be incorporated. TD values were extracted for all class pairs, 6 possible pairs from the four covers/uses and averages for all class pairs. These TD determinations were for the original radar backscatter values and Variance texture measures with different window sizes. The radar and radar texture values were then integrated for an overall evaluation. RESULTS Original Radar The transformed divergence values for the original backscatter PALSAR images are in Table 2. Most results are consistent in relation to the average DN and standard deviation values in Table 1. The greatest separability for all four polarizations is between the urban - water classes, with values ranging from 1917 to The greatest difference of DN values was also between water - urban land cover (see Table 1). Similarly, for three of the four original PALSAR bands, water is also significantly separable from both forest and residential. The exception is in the VV band, which gives poor separability in all categories except, as mentioned above, the urban and water features. Surprisingly, the urban land cover, which has significantly higher DN values than both residential and forest, are not as unique as would be expected. TD values for the class pair urban residential range from 124 to 619. Similarly the TD values for urban forest are also very low, ranging from 287 to This signifies there is not a strong separability measure between urban land cover and the other two classes. This could be the result of higher than expected land cover/use variation with the AOIs selected. The TD values between residential forest are also very low across all four bands, ranging from 119 to 279. This is not surprising given the DN values for both residential and forest are very similar due to the high level of vegetation in suburban development in this area that is represented in this class. It would be difficult to select a single band from the four PALSAR bands to do automated classification. The HV and VH bands perform very well separating water from urban, forest and residential. However, these two bands perform poorly when separating urban from residential and forest. Conversely, the VV band performs better at separating urban from both forest and residential, but much poorer when separating water from both urban and forest. The band with the best average TD value is the HH band. This band performs slightly lower than HV and VH when performing separability for water with the other three classes. However, all the TD values for water are above 1500, which indicates an acceptable delineation of classes. The HH also provides the highest urban residential and urban forest TD values of all the bands. However, these two values are too low to be considered acceptable for classification. Table 2. TD values for original backscatter values for Washington, D.C. PALSAR image Polar- Ization - HH HV VH VV As polarization bands are combined, the TD values tend to improve. The best average separability with the combination of two, three and four polarization bands are displayed in Table 3. When two bands are combined, the best average TD values occurs when the HH and HV bands are combined. Individually, for urban and residential areas, HH had a TD value of 619 and HV a value of 287. When the two bands were combined, the new TD value

6 was a much improved value of 915. Similar improvements were achieved for TD for the urban and forest areas, from individual values of 1059 and 365 to a combined TD value of Interestingly, as additional bands are added, some of the average separability decreases. Increasing polarization bands does not necessary improve the TD values. This may be due to introducing a third (or fourth) band that has a lower TD values than the previous two already combined bands. For example, the combined HH and HV urban residential yield a TD value of 915. When a third polarization band, VV, is added the overall TD value actually drops from 915 to 691. The single VV band had a TD value of 416. It may not be totally surprising that as the lower value is added to the combined HH and HV bands, the overall TD value decreases for the newly combined three band TD value. Similar results occur in the TD values when all four polarization bands are combined. The low TD value of 124 of the VH polarization band is added to the already combined HH, HV, and VV bands. The result is the TD values reduce from 691 to 513. Table 3. TD values for combined original backscatter values for Washington, D.C. PALSAR image Combined Polarization - Best Average Separability when two bands are combined HH and HV Best Average Separability when three bands are combined HH, HV, and VV Average Separability when all four bands are combined HH, HV, VH, and VV Radar Variance Texture Variance measure of textures for three different window sizes were calculated for each of the four PALSAR bands. The window sizes were 5 x 5, 7 x 7 and 11 x 11. TD values for each of the textures were calculated for all land cover/use pairs (Table 4). For all four land cover/use pairs there is an overall improvement with the texture TD over the original backscatter TD values. Also, as the window size increased, the TD values improved. The 11 x 11 texture provides the best results for TD values. Comparing the 11 x 11 texture TD against the original TD for the PALSAR image shows impressive improvements (Table 5). Table 4. TD values for three Variance texture windows for Washington, D.C. PALSAR image Polar- Ization - Texture created with a 5 x 5 window HH HV VH VV Texture using 7 x 7 window HH HV VH VV Texture created with a 11 x 11 window HH HV VH VV

7 Table 5. Differences between TD values of original and 11 x 11 texture Washington, D.C. PALSAR image Polar- Ization - HH HV VH VV Radar Fusion Combinations of the original radar data with the three texture sizes were examined. The goal was to determine if the overall TD values could be improved by adding texture bands to the original bands. The best improvement in the TD value is achieved by combining the 11 x 11 texture band with the original radar (Table 6). This combination provides increases over using either only the original bands (Table 7) or only the texture of an 11 x 11 window (Table 8). When combining the original PALSAR image and the 11 x 11 texture bands into a single analysis, the new TD values for the HV and VH polarizations are excellent. The HV and VH TD values for all land cover combinations except for residential forest have a TD value of The TD values for residential forest in HV and VH are not 2000, but still excellent, ranging from 1975 to The TD values for the HH and VV combined bands of the original image and 11 x 11 texture window shows substantial improvements over the original components, but do not perform as well as the HV and VH polarizations. Table 6. TD values for combined original and 11 x 11 texture for Washington, D.C. PALSAR image Polarization - HH HV VH VV Table 7. Differences between TD values of original and combined original and 11 x 11 texture Washington, D.C. PALSAR image Polar- Ization - HH HV VH VV Table 8. Improvements over using combined original and 11 x 11 texture versus only the texture 11 x 11 texture for the Washington, D.C. PALSAR image Polarization - HH HV VH VV

8 SUMMARY The original PALSAR data were limited in their ability to separate land cover/use for the Washington, D.C. scene. The extraction and calculation of Variance texture measures for each radar polarization resulted in consistently improving TD values. As the window size of the texture measure increased, the TD values also improved. Combining the original PALSAR image with the best of the texture images provided excellent TD values, implying excellent separability for classification. The results of this study show that texture measures extracted from quad-polarization radar data greatly improve separability between different land cover/use classes, suggesting that the use of radar texture measures will help to improve overall classification accuracy. ACKNOWLEDGEMENT The PALSAR imagery for this study was received by a data grant from the Alaskan Space Facility under sponsorship from NASA. REFERENCES Anys, H. and D. He, 1995, Evaluation of textural and multipolarization radar features for crop classification, IEEE Transactions on Geoscience and Remote Sensing, 33 (5): Banner, A. V. and F. J. Ahern, 1995, Incident angle effects on the interpretability of forest clearcuts using airborne C-HH SAR imagery, Canadian Journal of Remote Sensing, 2, (1): Dell Acqua, F., Gamba, P. and G. Lisini, 2003, Improvements to urban area characterization using multitemporal and multiangle SAR images, IEEE Transactions on Geoscience and Remote Sensing, 41 (9): Gauthier, Y., Bernier, M. and J. P. Fortin, 1998, Aspect and incident angle sensitivity in ERS-1 SAR data, International Journal of Remote Sensing, 19 (10): Herold, N., Haack, B., and E. Solomon, 2005, Radar spatial considerations for land cover extraction, International Journal of Remote Sensing, 26 (7): Haack, B., Solomon, E., Bechdol, M. and N. Herold, 2002, Radar and optical data comparison/integration for urban delineation: a case study, Photogrammetric Engineering and Remote Sensing, 68 (12): Hegarat-Mascle, S., Vidal-Madjar, D., Taconet, O. and M. Zribi, 1997, Application of shannon information theory to a comparison between L- and C-band SIR polarimetric data versus incident angle, Remote Sensing of Environment, 60 (2): Henderson, F., Chasan R., Portolese, J. and T. Hart Jr., 2002, Evaluation of SAR-optical imagery synthesis techniques in a complex coastal ecosystem, Photogrammetric Engineering and Remote Sensing, 68 (8): Kurosu, T., Uratsuka, S., Maeno, H. and T. Kozu, 1999, Texture statistics for classification of land use with multitemporal JERS-1 SAR single-look imagery, IEEE Transactions on Geosciences and Remote Sensing, 37 (1): Latty, R. S. and R. M. Hoffer, 1980, Waveband evaluation of proposed Thematic Mapper in forest cover classification, Proceedings of the Fall Technical Meeting, ACSM-ASP, Niagara Falls, New York, pp. RS- 2-D-1 to RS-2-D-12. Solber, A. H. S. and K. J. Anil, 1997, Texture fusion and feature selection applied to SAR imagery, IEEE Transactions on Geosciences and Remote Sensing, 10 (6): Swain, P. H. and S. M. Davis (eds.), 1978, Remote Sensing: the Quantitative Approach, McGraw-Hill, New York, 396 pages. Townsend, P. A., 2002, Estimating forest structure in wetlands using multitemporal SAR, Remote Sensing of Environment, 79: Toyra, J., Pietroniro A. and L. Martz, 2001, Multisensor hydrologic assessment of a freshwater wetland, Remote Sensing of Environment, 75: