Improving PolSAR Land Cover Classification With Radiometric Correction of the Coherency Matrix

Transcription

1 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 1 Improving PolSAR Land Cover Classification With Radiometric Correction of the Coherency Matrix Donald K. Atwood, Member, IEEE, David Small, Member, IEEE, and Rüdiger Gens, Senior Member, IEEE Abstract The brightness of a SAR image is affected by topography due to varying projection between ground and image coordinates. For polarimetric SAR (PolSAR) imagery being used for purposes of land cover classification, this radiometric variability is shown to affect the outcome of a Wishart unsupervised classification in areas of moderate topography. The intent of this paper is to investigate the impact of applying a radiometric correction to the PolSAR coherency matrix for a region of boreal forest in interior Alaska. The gamma naught radiometric correction estimates the local illuminated area at each grid point in the radar geometry. Then, each element of the coherency matrix is divided by the local area to produce a polarimetric product that is radiometrically flat. This paper follows two paths, one with and one without radiometric correction, to investigate the impact upon classification accuracy. Using a Landsat-derived land cover reference, the radiometric correction is shown to bring about significant qualitative and quantitative improvements in the land cover map. Confusion matrix analysis confirms the accuracy for most classes and shows a 15% improvement in the classification of the deciduous forest class. Index Terms Advanced land observing satellite (ALOS) PALSAR, land cover classification, polarimetry, remote sensing, synthetic aperture radar (SAR). I. INTRODUCTION L AND cover classification has primarily relied upon the use of imagery in the visible through shortwave infrared bands. The reason for this is the long-term availability of such datasets, maturation of techniques and tools, as well as the intrinsic adaptability of that spectral regime for distinguishing land cover types. The development of specialized indices, use of multitemporal datasets, and development of object-based classification have further improved classification accuracies; generally well in excess of 75%. The principal limitations of this technology are visibility issues (e.g., clouds, smoke, and shadows) as well as vegetation classes sharing similar spectral responses. Synthetic aperture radar (SAR) offers redress to both these limitations; offering all-weather, day/night coverage, as well as yielding information on the underlying structure of land cover. Important information can be extracted from single polarization SAR data, where signal brightness and texture can Manuscript received September 29, 2011; revised December 14, 2011; accepted January 11, D. K. Atwood and R. Gens are with the Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK USA (corresponding author, dkatwood@alaska.edu). D. Small is with the Remote Sensing Laboratories, University of Zürich, CH-8057 Zürich, Switzerland. Color versions of one or more of the figures in this paper are available online at Digital Object Identifier /JSTARS offer useful information on land cover. Howeve, the most significant gains have occurred with the recent availability of polarimetric SAR (PolSAR) data from sensors such as the -band ALOS PALSAR, -band Radarsat-2, and -band TerraSAR-X. Within the constraints of frequency, incidence angle, and spatial resolution, quad-pol data yields a complete characterization of the SAR microwave interaction with the target [1]. Incoherent target decompositions offer significant insight into land cover by characterizing each pixel by its scattering properties: surface bounce, double bounce, or volume scattering [2] [4] or entropy, alpha angle, and anisotropy [5]. RGB representations of these decompositions give immediate physical insight into the structure of landforms and can, at least qualitatively, be used to discriminate land cover classes. PolSAR land cover classification has undergone significant evolution over the last two decades. Initial approaches, such as [6] relied upon a segmentation of the image based on an arbitrary division of the entropy/alpha plane into eight classes or the entropy/alpha/anisotropy space into 16 classes. The issue with this approach was that land cover classes would often span more than one segment or several land cover classes would fall within a single segment. An improved approach to classification incorporated the Gaussian-based Wishart distribution to support supervised [7] and unsupervised [8], [9] classification of PolSAR data. The availability of the Wishart classifiers in polarimetric tools has led to their widespread use among those performing PolSAR classification [10], [11]. Challenges for PolSAR classification increase when topography is introduced. Topography, specifically azimuthal slopes, leads to a polarization orientation angle that affects the radar signature [12], [13]. While compensation techniques [13] [15] have been developed to remove this orientation angle, the effects of topography on SAR radiometry have not been adequately addressed. In the same way that cloud shadows impose challenges for optical classification, the dependence of SAR radiometry upon the satellite/topography geometry has significant impact upon the outcome of unsupervised Wishart classification of PolSAR data. The intent of this paper is to address the radiometric effects of topography and demonstrate how a radiometric correction of the coherency matrix can be used to improve classification accuracy. Section II presents the boreal environment and PolSAR datasets chosen to explore this effect. Section III demonstrates how variable response to topographic slope leads to multiple segmentations. Section IV describes our chosen image simulation -based approach to performing radiometric terrain correction. Section V outlines two processing paths: with and without radiometric compensation. Section VI compares the two approaches both qualitatively and /$ IEEE

2 2 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING quantitatively. Conclusions are drawn and recommendations for PolSAR classification are made in Section VII. II. PROJECT SITE AND DATASETS The primary focus of land cover classification for this study is the region surrounding Fairbanks, Alaska. Like much of interior Alaska, this boreal biome is characterized by rivers, wetlands, herbaceous tundra, very low-intensity urban development, as well as evergreen and deciduous forests. The deciduous forests are largely composed of quaking aspen (populus tremuloides) and paper birch (betula papyrifera); residing either on south-facing slopes or in previous evergreen forests that have been subjected to wildfire. The evergreen forests are dominantly white spruce (picea glauca) and black spruce (picea mariana), with the black spruce populating the north-facing slopes characterized by permafrost. Aside from simple phenological characterization of land cover, one must consider the impact of surficial water. The ubiquitous presence of permafrost means that many flat slopes manifest significant water in the understory, leading to a land cover classification of woody wetlands. The polarimetric SAR data used to explore this region was acquired by the advanced land observing satellite (ALOS). The -band PALSAR SAR sensor on ALOS acquired data in one of four modes: single-pol, dual-pol, quad-pol, and ScanSAR. For the case of the study area, quad-pol measurements had been made in April, May, July, and November. Of these, the acquisition of 12 July, 2009, was selected. The decision was based upon the seasonal dynamics of interior Alaska. April and May are months of river breakup and snowmelt and thus are characterized by significant regions of standing water and wet snow. November, on the other hand, is characterized by early winter snowfall of variable thickness. July was selected to offer more stable environmental conditions of leaf-on and post-thaw. The reference land cover selected for this project is the U.S. Geological Survey (USGS) National Land Cover Data (NLCD) [16]. This comprehensive land cover map for the United States is derived from 30-m Landsat imagery acquired in The NLCD is subject to some errors in classification and classes may have changed between 2001 and the ALOS acquisitions. In addition, registration errors of up to one pixel may occur, leading to misassociations at the edges of classes. However, these errors are small in the statistical sense. By having access to classification for every pixel in the scene versus a small number of ground control points, we are better able to establish the spectral characteristics for all pertinent land cover classes. III. IMPACT OF TOPOGRAPHY ON CLASSIFICATION The impact of topography can be readily seen in the outcome of an unsupervised PolSAR classification. Here the complex Wishart classifier is used due to its availability in thepolsarpro Polarimetric Tool [17] and the fact that it makes full use of the polarimetric coherency matrix. The method offers unsupervised classification based on polarimetric target decomposition [5] and a maximum-likelihood classifier based on the complex Wishart distribution [8]. (a) Fig. 1. Comparison of (a) the polarimetric span with (b) the associated Wishart H/A/alpha segmentation. Segments are seen to be highly correlated with SAR intensity and independent of actual land cover. One advantage of the Wishart classifier is that the initial identification of clusters is based on scattering mechanisms derived from the Cloude Pottier target decomposition. The initial segmentation is based on either: 1) segmenting the entropy/alpha angle plane into eight different zones or 2) segmenting the entropy/alpha angle/anisotropy block into 16 different zones. This means that the initial segmentation of a quad-pol image is independent of the radiometric variation associated with topography, since entropy, alpha angle, and anisotropy are invariant of the polarimetric span (trace of the coherency matrix). Individual pixels are segmented only by their Cloude Pottier scattering properties. The Wishart algorithm is a maximum-likelihood classifier in which a distance measure is established between each pixel s coherency matrix and the respective cluster means in an iterative process. The process begins with the eight or 16 segment clusters described in the previous paragraph. Distance measures are computed between each pixel in the image and all the cluster means, then each pixel is reassigned to its nearest cluster. New cluster means are then computed, and the process is repeated. The iterative process terminates when the percent of pixels switching classes becomes smaller than a predetermined value or a maximum number of iterations is reached. The iteration tends to converge rapidly, and in the process the cluster centers migrate significantly from their original eight or 16 locations. The distance measure is based largely on the value of the Pauli decomposition elements (diagonal elements of the coherency matrix) and to a lesser extent, the off-diagonal matrix elements. What this means is that the assignment of pixels to clusters does depend on SAR intensity. Not surprisingly, bright pixels on slopes facing the SAR satellite will have quite different spectral characteristics than dark pixels on the backside of hills. The result is that, at least in principle, identical land cover types may become members of different segment clusters based upon their relative position in the SAR/topography geometry. This effect is clearly shown in Fig. 1, where both the SAR intensity (span) and associated Wishart segmentation (b)

3 ATWOOD et al.: IMPROVING POLSAR LAND COVER CLASSIFICATION WITH RADIOMETRIC CORRECTION OF THE COHERENCY MATRIX 3 Fig. 2. Illustration of the reference areas for the three radar backscatter conventions: beta, sigma, and gamma naught (taken from [21]). are shown for a localized, hilly portion of the quad-pol scene. Comparison of the two images shows a clear correlation between intensity and segmentation, even in cases where the land cover class is homogeneous. It is this allocation of segments to intensity, rather than land cover type, that suggests the need for an alternative approach to PolSAR land cover classification. In the following sections, a robust means for performing radiometric correction of the coherency matrix is introduced. IV. RADIOMETRIC CORRECTION: TERRAIN-FLATTENED GAMMA NAUGHT In the preceding section, the extent to which topographic variations can dominate the radiometric backscatter signal retrieved from spaceborne SARs is demonstrated. Topography dominates the backscatter so strongly because the local area illuminated within each (azimuth, slant range) bin can easily vary by db. Without compensation for terrain effects, the more subtle signatures induced by land cover often become lost. Moreover, the effects of foreshortening, layover, and shadow can conspire to make the recovery of land cover signatures a difficult proposition. The reference areas used within the,, and backscatter conventions are illustrated in Fig. 2. Each convention employs its own definition of the backscatter reference area, placing it in: 1) the slant range plane itself ( with area ) [18]; 2) the ground as modeled by an ellipsoidal Earth ( with area ); or 3) the plane perpendicular to the local look direction ( with area ) [26]. Many have observed brightness dependencies on incident angle, in each of the three conventions. The trend is strongest in, reduced but still present in, and further reduced in. Conventionally, many have attempted to moderate this trend by replacing the use of the look angle in the convention with a so-called local incident angle [19]. This section outlines a more complex methodology that adheres strictly to the gamma naught backscatter convention [20]. Approaches based on the local incident angle all fail to account for the nonhomomorphic nature of the relationship between the radar s native azimuth and slant range grid space and geographic map coordinates. There are many-to-one connections between those spaces in foreshortened and layover regions and many-to-none in radar shadow regions [21]. Indeed, even the idea of the incident angle is a flawed concept, as in hilly terrain, in which many local incident angles often contribute to a single radar image pixel (pyramids aside). Given the evident lack of bijectivity outside of flat areas, assuming homomorphism between radar and map geometries ensures an unrealistic model of how the backscatter was actually generated. An alternative backscatter convention that makes use of a radar image simulation to carefully estimate the local illuminated area at each grid point in radar geometry was introduced in [20]. A detailed description of the methodology together with results indicating that the flattening achieved is clearly improved is described in [21]. Summarizing, the local area contributing to each radar geometry grid point (indices: azimuth and range ) is integrated by iterating through all relevant easting ( ) and northing ( ) DEM coordinates, geolocating each point to find the proper corresponding radar image coordinates and, and outputting a raster in radar geometry equal to the sum total as follows: The geolocation proceeds using either the zero-doppler time annotation convention (widely used) or Doppler-centroid time (JAXA PALSAR product s squinted geometry is not deskewed). The choice must conform to the SAR image product being normalized to allow accurate geolocation. However, the azimuth timing annotation convention should not impact the quality of the flattening achieved. Any points in the DEM not visible to the radar (occluded by radar shadow) are kept from contributing by performing a shadow check before each sum operation [21]. The local area is calculated within the gamma naught convention by strictly adhering to the definition of local contributing area as always being in the plane perpendicular to the local slant range direction. For ground range products,anextrascalingstepis performed [21] to transform the radar geometry s native slant range reference areas into the necessary ground range geometry. Only by including the above steps in the image simulation is one able to adequately mimic the radar image s formation process, whereby each radar pulse is convolved with the landscape. Approaches based on concepts of the local incident angle are unable to properly model the actual radar image formation process. Using area rather than angle, one is able to achieve a better description of how the radar sees the ground. Operating with angles rather than areas implicitly assumes that infinite planes are directly relevant to backscatter normalization. But it is the sum of the finite contributing areas that describes the amount of local terrain actually visible to the radar at each range and azimuth coordinate. By their nature, these areas encompass influences from both range and azimuth slopes: angle-based methods must attempt to tally those impacts separately [28]. The local contributing area is collected in the radar geometry of the input product, i.e., slant range for JAXA CEOS SLC products. The radiometry of the SAR backscatter product to be normalized should first be transformed into the beta naught backscatter convention [29] if not already there, particularly for (1)

4 4 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (a) Fig. 3. Comparison of the polarimetric span of a hilly portion of the scene (a) before and (b) after application of the radiometric correction. The strong terrain-induced effects, clearly visible on the left, are removed in the image with radiometric flattening. Radiometric scale: black 13 db, white 0.5 db. products that cover wide swaths with large differences between the incident angles at near and far range. Once the DEM integration is complete and the image simulation is available, one simply executes the normalization by undoing the radar product s initial normalization convention (e.g., with a scalar constant area in convention [18]) and then dividing by the locally appropriate reference area that was calculated within the gamma naught convention as follows: where the denotes terrain rather than for ellipsoidal correction. The conventional gamma naught widely used in the literature is distinguished as. Radiometrically terrain corrected (RTC) images, as described here, conform to the definition of terrain-flattened gamma naught [21]. The scalar constant is simply the product of the azimuth and slant range sample intervals. Fig. 3 shows the polarimetric span (all polarizations contributing) both before [Fig. 3(a)] and after [Fig. 3(b)] radiometric normalization. Although the normalization described above is performed in radar geometry, it can also be applied in map geometry if both the and rasters are themselves first terrain-geocoded. Performing the normalization in radar geometry allows one to avoid having to terrain-geocode (the image simulation). Once the UZH method [21] for terrain-flattening the backscatter has been applied, the backscatter coefficients become available for further processing. V. POLARIMETRIC PROCESS FLOW Polarimetric processing in this project entailed the use of two, free, open-source software tools: the European Space Agency s (ESA) PolSARpro SAR Data Processing and Educational Tool [17] and the Alaska Satellite Facility s (ASF) MapReady Remote Sensing Toolkit [22]. The former provides a wealth of polarimetric capabilities, while the latter performs terrain correction and readies the PolSARpro products for ingestion into a GIS. PolSARPro is the most comprehensive open-source software for processing PolSAR and polarimetric interferometry (b) (2) (PolInSAR) data. It offers numerous techniques and options, as well as tutorials and background material. PolSARPro supports a large variety of spaceborne and airborne data sources. As its focus is entirely on the polarimetric processing, it does not contain means of geocoding its results. Thus, it is ill-suited for GIS implementation on its own. MapReady was developed to facilitate the use of SAR data by researchers and operational agencies. The MapReady processing flow includes terrain correction of SAR data using a digital elevation model (DEM) to remove the distortions caused by the side looking geometry, geocoding into a number of predefined standard map projections, as well as export into GIS-compatible GeoTIFF format. Through cooperative software development, PolSARpro and MapReady have been made to function together for combined processing. In the simplest implementation, PolSARpro is used to perform all polarimetric processing, and then MapReady is used to convert the polarimetric products (e.g., decompositions and segmentations) into terrain-corrected and map-projected GeoTIFFs. In an alternative path, MapReady is used to terrain-correct and geocode the T3 Coherency matrix, after which PolSARpro is used to generate polarimetric products. Then, MapReady converts those products to a GeoTIFF format. A statistical study of these two paths, shows their functional equivalence [23]. For this project, the former path was chosen for its operational efficiency and conceptual simplicity. The basic process flow for this project consisted of the following. 1) Generation of T3 coherency matrix from single look complex (SLC) PALSAR data. In this step, the polarimetric data is multilooked with 7 azimuth and 1 range looks. This yields approximately square pixels in ground range and provides the ensemble averaging necessary to perform polarimetric decomposition. 2) Speckle filtering using a Lee Sigma filter [24] using the default values (3/9/0.8) in PolSARpro. An investigation of speckle filtering approaches showed the Sigma filter to offer optimal smoothing, while maintaining spatial resolution in regions of heterogeneity. 3) Polarimetric orientation angle (POA) compensation [15] to address the rotated dihedrals associated with urban areas and the effects of azimuthal slopes in areas of topography. 4) In PolSARpro, entropy (H), alpha angle, and anisotropy (A) is produced and Wishart unsupervised segmentation (as described in Section II) is performed on the conditioned coherency matrix. The sixteen segmentations from this process provide the basis for accessing classification accuracy in Section VI. 5) Using MapReady, the polarimetric products of PolSARpro are terrain corrected and geocoded. Since the geolocation accuracy of the result is largely dependent upon the quality of the digital elevation model (DEM), a 10-m posting DEM, photogrammetrically derived from ALOS PRISM, is used. This result offers both better accuracy and higher resolution than the alternative National Elevation Dataset (NED) DEM used for most of Alaska. To investigate the impact of performing radiometric correction on the coherency matrix, we introduce a modification to the process flow described above. In the modified path,

5 ATWOOD et al.: IMPROVING POLSAR LAND COVER CLASSIFICATION WITH RADIOMETRIC CORRECTION OF THE COHERENCY MATRIX 5 (a) Fig. 4. Comparison of Yamaguchi four-component decompositions [Double (red), Volume (green), Surface (blue)] of a hilly portion of the scene (a) before and (b) after application of the radiometric correction. The obvious manifestations of SAR geometry are removed in the radiometrically flattened decomposition. the coherency matrix is normalized for local illuminated area (adhering strictly to the gamma naught backscatter convention) immediately after the extraction of the T3 matrix elements. Each element of T3 is divided by the local area, as described in the preceding section. Subsequent processing then occurs on data with radiometric flattening. To better exhibit the impact of this radiometric flattening on polarimetric products, a four-component Yamaguchi decomposition was performed on both the nonnormalized and normalized matrices. The decompositions were performed immediately after the POA compensation step and the results are shown in Fig. 4. The introduction of the radiometric normalization offers two advantages. First, since it corrects each pixel s coherency matrix prior to Lee Sigma filtering, the filtered coherency matrix should be a better representation of local polarimetric properties; unbiased by topographic variability. Second, by normalizing the total reflected power in the Yamaguchi decomposition, the representation of scattering mechanisms is unbiased by topography and offers deeper insight into the characteristics of the land cover. The effect is clearly seen in the figure. VI. ACCURACY ASSESSMENT OF LAND COVER CLASSIFICATION The result of the H/A/Alpha Wishart unsupervised classification is 16 segments that represent distinctive polarimetric scattering properties. To assess the impact of radiometry upon this process, we investigate how the Wishart classification deals with the same land cover in areas of high reflectance versus areas of low reflectance. The way this is addressed is to use GIS hillshade tools and the project area DEM to create a model of the Earth s reflectance as seen from the satellite s position. Hill slopes facing the satellite (front slopes) are brighter, whereas slopes facing away from the satellite (back slopes) are darker. Limiting the focus to these sloped areas creates two distinct regions within the PolSAR image, distinguished largely by satellite radiometry. Since the ALOS satellite travels in an approximate northerly direction (345 degrees True), there should be no systematic difference in the land cover for these two regions. The reason for (b) this is that while natural ecosystem differences exist in Northversus South-facing slopes, no intrinsic differences exist between East- versus West-facing slopes. Thus, one hopes that the Wishart classifications for the two regions should be very similar. However, if radiometry plays a significant role in the Wishart classification process, these unsupervised classification signatures are expected to diverge. By comparing the resultant class populations, we can determine the extent to which radiometry has an adverse impact upon classification. And by performing the same comparison after radiometric normalization has been applied, we can further investigate the extent to which radiometric normalization assists the Wishart classification process by removing the effects of topography. Fig. 5 shows the impact of satellite geometry (front slope versus back slope) upon Wishart classification results for both before and after application of radiometric normalization. For the case without normalization, the relative populations of Wishart classes are seen to differ significantly with little overall correlation. This confirms the earlier qualitative statement that variable radiometry spawns multiple Wishart classes, even for areas of assumed identical land cover. Significant improvement in the front slope/back slope correlation is seen after application of the radiometric normalization. Wishart classes 4, 5, and 8 are the dominant classes for both the front and back slope regions and constitute more than 76% of total number of pixels. The fact that the population distributions are not exactly identical is caused by: 1) actual asymmetries between the front and back slope regions and 2) the fact that polarimetric properties have dependence on local incidence angle. Although radiometric terrain-flattening cannot completely remove the impact of satellite look geometry, these results indicate that it is an effective first-order method for reducing the impact of topography on Wishart classification. Final classification after Wishart segmentation entails assigning these segments to specific land cover classes. Using the NLCD 2001 land cover classes as reference, cluster busting of the segmentations is performed. Of the 16 segmentations, most contain large numbers of pixels, many represent the same land cover class, and several segmentations have small populations and cannot be clearly correlated with any given land cover class. In assigning segmentations to land cover classes, maximized correlations with the NLCD 2001 reference classes are sought. Each segmentation is identified as that NLCD reference class for which the statistical correlation is highest. A consequence of this pairing process is that not all land cover classes in the NLCD are represented in the final PolSAR classification. For example, the mixed forest class cannot be independently identified via PolSAR and becomes included in the deciduous forest land cover class. Likewise, shrub/scrub, herbaceous wetlands, and barren land classes in the NLCD are not recognizable in the PolSAR segmentation. Consequently, the PolSAR classification is limited to the following classes: open water, developed land, deciduous forest, evergreen forest, and woody wetlands. It should be noted that developed land poses particular challenges for PolSAR land cover classification. Although the NLCD draws distinctions between low, medium, and high intensity development, no such distinction can be identified

6 6 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (a) (b) Fig. 5. Graphs showing the relative populations of the 16 Wishart classes for regions facing the satellite (front slopes) versus regions facing away from the satellite (back slopes). (a) Results without normalization. (b) Results with radiometric normalization. Fig. 6. Comparison of the conventional Wishart classification without terrain-correction (left), NLCD 2001 land cover classification (middle), and radiometricallycorrected Wishart classification (right). Both Wishart classifications under-represent urban areas (in red) and misclassify airports as open water (in blue). from PolSAR data. Moreover, urban areas are frequently misclassified as forests. As noted by [15], buildings viewed from nonorthogonal directions behave as rotated dihedrals with a result that they have large HV polarizations characteristic of forests. Similar challenges exist for paved areas. Fairbanks has two airports, one civilian and one military. These large flat regions support specular reflections of the incident microwaves, and for that reason are segmented along with the class of open water. Any rigorous use of PolSAR for land cover classification must address urban areas with special treatment, using independent algorithms to identify developed land, before the other nonurban areas are classified through more traditional means. Fig. 6 shows the results of the two Wishart classifications (with and without radiometric-correction) compared with the NLCD reference land cover classification. Both Wishart classifications satisfactorily identify woody wetlands, but have the previously noted limitations with respect to developed areas. With the exception of the misidentified airports, both Wishart classifications yield nearly identical open water areas. This result is not surprising considering that topography plays no role in backscatter from water. Lack of agreement between the Wishart and NLCD 2001 open water class is consistent with the ever-changing nature of the braided Tanana River. The significant difference between the two Wishart classifications is most evident in the deciduous and evergreen forest classes. The radiometrically corrected Wishart classification is seen to offer significantly better qualitative agreement with the NLCD. The uncorrected classification exhibits front slope versus back slope asymmetry that is driven by the SAR radiometry, while the corrected classification shows all the forest

7 ATWOOD et al.: IMPROVING POLSAR LAND COVER CLASSIFICATION WITH RADIOMETRIC CORRECTION OF THE COHERENCY MATRIX 7 TABLE I CONFUSION MATRIX FOR WISHART CLASSIFICATION WITHOUT RADIOMETRIC CORRECTION TABLE II CONFUSION MATRIX FOR WISHART CLASSIFICATION WITH RADIOMETRIC CORRECTION type spatial trends of the reference dataset. Thus, in those areas where topography would be expected to make a difference, the use of radiometric correction introduces a clear qualitative advantage over normal Wishart classification. In the flatter areas, both uncorrected and corrected classifications exhibit the same strengths and weaknesses that typify PolSAR classification. More quantitative evaluations of the two Wishart classifications are shown in Tables I and II. Using the Comparison tool in ArcGIS, truth tables are established for the Wishart classifications using all image pixels. As noted previously, the Producer Accuracies for mixed forest, barren land, shrub/scrub, and herbaceous wetlands are all zero. This fact, coupled with the accuracy limitations of the NLCD itself (stated to be in the 70% range), suggest that classification accuracy values should be used for comparison purposes only. Table III compares the producer accuracies for the Wishart classifications with and without

8 8 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING TABLE III PRODUCER ACCURACIES FOR SELECTED CLASSES, AS WELL AS DIFFERENCE BETWEEN WISHART CLASSIFICATION WITH AND WITHOUT RADIOMETRIC CORRECTION radiometric correction. Except for woody wetlands, in which the radiometric correction yields a 4% lower accuracy, the radiometric correction yields equal or better classification results. The most pronounced improvement for the deciduous forest class in which the improvement in accuracy is 15%. VII. DISCUSSION AND CONCLUSION It is important to note that the radiometric correction described in Section IV works best when flattening cross-polarizations [27]. The use of this normalization for all elements of the coherency matrix presupposes that each scattering mechanism s backscatter is equally affected by the local illuminated area. However, the relative proportions of surface, double bounce, and volume scattering can also be influenced by local incidence angle and slope aspect, with slightly different influences on each polarization. Future developments of the topographic correction should address the complex interplay between look angle, topography, and land cover to achieve the goal of leaving each pixel with a normalized response that depends only on land cover; independent of topography. That task is beyond the scope of this paper and exceeds current polarimetric capabilities. Moreover, such an approach entails an aprioriknowledge of the land cover classification in order to properly model the radiometric and polarimetric response. Since land cover classification is the goal, we have taken a first order approach to its discovery. Modifications to the scattering mechanism are limited to POA compensation through rotation of the coherency matrix [15]. The benefitofpoacompensation for classification has already been demonstrated by [25]. Finally, the radiometric impact of topography is addressed by applying a single normalization value to all elements of the T3 matrix. Both the qualitative and quantitative results in the preceding section show an advantage of radiometric correction prior to Wishart classification. As noted in Section II, the entropy, alpha angle, and anisotropy of each pixel, used as initialization for the Wishart classification, are independent of SAR intensity. Thus, radiometric correction has no impact upon the initial selection of cluster centers. However, all subsequent computations of cluster centers rely upon the mean coherency matrix of the cluster constituents. It is clear that the mean and, more importantly, the distribution of these clusters do depend upon the span. Taking the example of the distribution of pixels associated with a birch forest, it is clear that the radiometric variability associated with terrain will serve to broaden the pixel distribution of birch trees in the polarimetric n-space. Since all classification processes rely upon the separability of clusters, cluster broadening caused by topography will most likely lead to reduced classification accuracy. Clusters representing different land covers, distended by span, are more likely to overlap and lead to confusion between classes. This is clearly demonstrated in separation of birch and spruce forests in Fairbanks, where radiometric correction clearly led to improved class separability. In the absence of class-specific approaches to addressing the topographic effects upon scattering mechanisms, it seems prudent to perform a universal radiometric flattening of the coherency matrix for all PolSAR classification in regions with significant topography. A final observation of this study is the importance of using polarimetric tools that permit the polarimetric processing of SAR data all the way to terrain-corrected and map-projected GeoTIFFs. Any supervised classification or use of ground control points for assessment of classification requires high geolocation accuracy of the PolSAR products. This necessitates getting both the PolSAR products and ancillary datasets into a common GIS environment. The complementary, open source tools of PolSARpro and MapReady, as well as a new generation of commercial software, provide the means to accomplish this, extending the utility of PolSAR data for geoscience applications. ACKNOWLEDGMENT The authors would like to thank Prof. E. Pottier for the polarimetry course that he offered in Fairbanks, as well as his significant efforts to merge the capabilities of the PolarSARpro and MapReady software tools. The constructive comments of two anonymous reviewers were also appreciated. 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Ulander, Radiometric slope correction of synthetic aperture radar images, IEEE Trans. Geosci. Remote Sens., vol. 34, no. 5, pp , Sep [29] D. Small, M. Jehle, A. Schubert, and E. Meier, Accurate geometric correction for normalisation of PALSAR radiometry, in Proc. ALOS Symp., Rhodes, Greece, Nov. 2008, p. 7. Donald K. Atwood (M 95) received the Ph.D. degree in physics from the Massachusetts Institute of Technology (MIT), Cambridge. He was with AT&T Bell Laboratories and Raytheon s Research Division, where he worked in the field of microlithography. In 1992, he joined MIT Sea Grant, where he developed Autonomous Underwater Vehicles with specialization in underwater acoustics. He then worked for Raytheon, first as a Technical Advisor for international environmental programs, then as a Manager in atmospheric remote sensing at the Goddard Space Flight Center. In 2000, he became the Director of Science Support for the U.S. Antarctic Program. He is currently the Chief Scientist with the Alaska Satellite Facility (ASF), Geophysical Institute, University of Alaska Fairbanks, where he focuses on applications of SAR, PolSAR, and interferometry for Earth Science. At ASF, he promotes the broader adoption of SAR though short courses at international conferences, development of new SAR tools, and educational outreach programs. Dr. Atwood is a member of the American Geophysical Union and the American Society of Photogrammetry and Remote Sensing. David Small (S 86 M 98) was born in Ontario, Canada. He received the B.A.Sc. degree in systems design engineering from the University of Waterloo, Waterloo, ON, Canada, in 1988, the M.A.Sc. degree in electrical engineering from the University of British Columbia (UBC), Vancouver, BC, Canada, in 1991, and the Ph.D. degree from the University of Zürich (UZH), Zürich, Switzerland, in He is currently a Senior Research Scientist and Co-Leader of the SAR group within the Remote Sensing Laboratories, University of Zürich. In the past, he has researched improvements to generation of height models using cross-track radar interferometry, and geometric calibration and validation for a variety of SAR sensors. He is presently researching the extension of radar image simulation software to incorporate further mode-specific parameterizations and monitor systematic signatures of snow melt over large areas. He is a member of the European Space Agency s Quality Working Group for SAR sensors. Dr. Small is a member of the IEEE Geoscience and Remote Sensing Society. He was chair of the CEOS SAR Calibration/Validation Workshop in Rudiger Gens (M 03 SM 09) received the M.Sc degree in surveying and mapping and Ph.D. degree in engineering from the University of Hannover, Hannover, Germany, in 1994 and 1998, respectively. He is currently a Remote Sensing Scientist with the Alaska Satellite Facility (ASF), Geophysical Institute, University of Alaska Fairbanks (UAF). He is cooperating faculty member with the Department of Geology and Geophysics at UAF since 2005, where he teaches courses in the principles and applications of SAR and InSAR. His research specialization is in the processing and applications of SAR, PolSAR, and InSAR data, and their synergistic use with other remote sensing and GIS datasets. At ASF, he has developed a variety of software tools for making SAR data more accessible and usable, has provided technical support to a large remote sensing user community, and has used satellite data to improve understanding of the changing Arctic landscapes and the processes guiding these changes. He is an associate editor for the International Journal of Remote Sensing and co-chair of the ISPRS VII/2 working group on SAR interferometry. Dr. Gens is member of the American Geophysical Union and the Dutch Society for Remote Sensing and Geoinformation.