Polarimetric SAR Data in Land Cover Mapping in Boreal ... - IEEE Xplore

3652

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 48, NO. 10, OCTOBER 2010

Polarimetric SAR Data in Land Cover Mapping in Boreal Zone Anne Lönnqvist, Yrjö Rauste, Matthieu Molinier, and Tuomas Häme

Abstract—This paper compares ALOS PALSAR fully polarimetric and dual-polarized data in the application area of land cover mapping. To assure versatile comparison of the data, different classification methods and different features of data are used. Two of the classification methods used are based on supervised classification and two on unsupervised classification. Polarimetric data are used in three ways: 1) as fully polarimetric data; 2) features calculated from fully polarimetric data; and 3) intensity data of selected channels. Combinations of six (water, field, sparse forest, dense forest, peat land, and urban areas), five, four, and three classes were used for classification. Fully polarimetric data gave better results (87.5%–84.7% with three classes; open land areas, forest, and water) than intensity data only (83.6%–78.6%), but the differences in the overall accuracies between the methods were not more than 7.6%. Kappa coefficients of agreement are moderate for all the classifications. Supervised classification can be expected to perform better than unsupervised classification, given that the training areas can be selected accurately. Dual polarization data were found to be an attractive alternative in cases where fully polarimetric data are not available or it is of low resolution. With intensities of selected polarimetric features, it was possible to obtain a high classification accuracy as with fully polarimetric data. This also opens possibilities for nonspecialist users to benefit from polarimetric information in classification. Index Terms—Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR), classification, land cover, polarimetric synthetic aperture radar.

I. I NTRODUCTION

T

HREE new spaceborne synthetic aperture radar (SAR) sensors: the Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR), TerraSAR-X, and Radarsat 2, provide fully polarimetric images, which can be used for land cover mapping. Optical satellite imagery has been used extensively to monitor land cover. Optical data can be of limited use in certain regions of the globe where cloud cover or darkness limits acquisitions (e.g., at high latitudes, tropics). SAR, with its day-and-night, all-weather observation capability, is particularly useful in these regions. Manuscript received December 17, 2008; revised July 8, 2009, October 2, 2009, and February 17, 2010. Date of publication June 1, 2010; date of current version September 24, 2010. This work was supported by the National Technology Development Agency of Finland (TEKES) in the context of project NewSAR. A. Lönnqvist, Y. Rauste, and T. Häme are with the VTT Technical Research Centre of Finland, 02044 Espoo, Finland (e-mail: [email protected]). M. Molinier is with the VTT Technical Research Centre of Finland, 02044 Espoo, Finland and also with the Laboratory of Computer and Information Science, Helsinki University of Technology, 02015 Espoo, Finland. Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TGRS.2010.2048115

ALOS PALSAR, TerraSAR-X, and Radarsat 2 are expected to advance methods in many application fields, for example, measuring global forest distribution, monitoring the changes of agricultural production, measuring biomass density and its variation, and disaster monitoring. All these applications need automated interpretation and classification of data to assist decision making. Different methods for classifying polarimetric data have been studied theoretically and tested with airborne polarimetric data. The effect of data type and frequency has also been considered. Cloude et al. [1] and [2] introduced a classification scheme where polarimetric SAR data were divided into three orthogonal components, and these components were used when classifying data. Entropy (H) is a measure of randomness of a scatterer, alpha (α) is the averaged backscatter angle and can be used to identify the average scattering mechanism. Anisotropy (A) can be used to detect single or multiple target return. Lee et al. [3] suggested a classification technique, which is a combination of unsupervised classification based on polarimetric entropy-alpha decomposition and the maximum likelihood Wishart classifier. This was further developed by Ferro-Famil et al. [4] and also applied to single- and dual-frequency data (NASA JPL AIRSAR sensor P-, L-, and C-bands). Dual-frequency data were estimated to be superior compared to single-frequency data, but no overall classification accuracies were presented. Lee et al. [5] compared fully polarimetric versus dual- and single-polarization SAR data for landuse classification. Crop and tree age classification were used as application areas. L-band polarimetric data were found to be the best in case of crop classification (overall accuracy of 81.65%) and P-band in tree age classification (79.16%). Multifrequency operation was highly encouraged. Lee et al. [6] proposed an unsupervised classification scheme with the Freeman and Durden decomposition [7]. First, pixels were divided on the basis of dominant scattering mechanism. Then, the classification was done in these groups with iterative Wishart classifier. This was applied by Lumsdon et al. [8] to fully polarimetric L-band data. They also compared H/A/α decomposition and Freeman decomposition-based classification results and concluded that both work equally well for water areas, but there are differences in areas of volume and surface scattering. The accuracy between Freeman-classified image and actual land cover classes from an optical satellite image was found to be 30% using 16 classes. The correlation accuracy between the two classification methods was found to be 65%, but the overall accuracy for H/A/α-based classification was not mentioned. One example of classification using fully polarimetric data was presented by Skriver et al. [9]. Data acquired by the Danish, fully polarimetric EMISAR system (an L- and C-band, airborne

0196-2892/$26.00 © 2010 IEEE

LÖNNQVIST et al.: POLARIMETRIC SAR DATA IN LAND COVER MAPPING IN BOREAL ZONE

SAR) were used to study classification with the Wishart distributed covariance matrix. Pixel spacing of the 10-look data was 5 m by 5 m. Six different acquisitions were used. Using both bands for classification decreased classification errors when compared to single-frequency performance. Best performance was obtained for lake and crop classes (classification error below 10%) and worst for village class (even 90% error). Grassland, deciduous, and conifer forest had classification error below 40%. Dobson et al. [10] used hierarchical, knowledge-based decision rule with a majority filter for classification. They combined data from two different sensors: ERS-1 (C-band, VV polarization), and JERS-1 (L-band, HH polarization). Three classes were used: 1) surface; 2) short vegetation; and 3) tall vegetation. The test site had less than 1% pixels containing urban class so it was not taken into account in the classification. Tall vegetation was divided into three classes of trees: 1) decurrent and broadleaf (i.e., oaks); 2) excurrent (i.e., pines) and long needle-leaf; and 3) excurrent and short needle-leaf. Overall accuracy of over 90% was obtained with five classes when using combination of both sensors. When using the L-band JERS-1 alone, the overall accuracy of the classification was 65.9%. A comparison of SAR classifiers is also presented in the paper. Pierce et al. [11] used the same kind of classification for polarimetric AirSAR data. Accuracy of over 90% was reported for three classes (bare, short, and tall vegetation), but class urban was left out as it was not possible to evaluate classification accuracy accurately for that class. Hoekmann et al. [12] used multiband polarimetric airborne data for forest type characterization. They were able to differentiate 15 classes with overall accuracy 68%–94%. The main classes were: 1) high forest; 2) low forest; and 3) palm forest, which were divided further according to flooding situation and biomass content. Freitas et al. [13] studied land use and land cover mapping in the Amazon using airborne polarimetric P-band SAR data. They applied iterated conditional mode (ICM) contextual classifier to amplitude, intensity images, biomass index, and some polarimetric parameters. Using four classes (three levels of biomass and floodplain), they were able to obtain overall accuracy of 82.72% and a Kappa coefficient of agreement of 76.81% with bivariate intensities HH-HV. They concluded that only these two channels are needed to get most of P-band data for tropical forest land cover discrimination purposes. Bruzzone et al. [14] achieved with RBF neural architecture overall accuracy of 92.15% with classes forest, urban, water, and fields when using both temporal variability and long-term coherence in the classification. The classification was done on a set of eight ERS complex SAR images (C-band, VV polarization). A digital land use inventory and a Thematic Mapper image (acquired by the Landsat 5 satellite) were used for validation. Temporal variability, combined with average amplitude and texture, was also used in for change detection [15]. In this paper, ERS-1 and ASAR imagery were used. Texture was mainly effective in separating built-up areas [16]. Overall accuracy was 95.6%, but per-point accuracy of change detection was poor. These results are difficult to compare with each other, since the number of classes, frequency bands and polarizations differs from paper to paper and the results and the optimal combination

3653

Fig. 1. Pauli [1] color-coded presentation of ALOS PALSAR data acquired in November 2006 from the Kuortane test site. HH-VV is represented by red, HV by green, and HH+VV by blue color. The scene is 35 km in range and 70 km in azimuth. JAXA and METI 2006.

of channels, change. When dealing with new kind of data, the optimal feature combination has to be reconsidered. In this paper, performance of spaceborne fully polarimetric ALOS PALSAR data in the field of land cover classification is studied. It is also compared with dual polarization data and the effect of multitemporality is considered. Four land cover classification methods, two supervised and two unsupervised are used for the comparison. Wishart H/A/α classification [6] is used in the unsupervised classification of fully polarimetric data. Wishart statistics are used for the supervised classification of fully polarimetric data. Intensity data of the polarimetric channels are used for both unsupervised and supervised classification. Also, the possibility to use amplitude of polarimetric features for land cover mapping is studied. This approach enables the use of old amplitude-based classification methods and still includes more polarimetric information to the analysis than just the amplitudes of the channels. II. M ATERIALS AND M ETHODS A. Test Site The test site is in Kuortane, located in the central Finland (62◦ 48 33 N, 23◦ 30 50 E). The center of Kuortane is located on the east side of lake Kuortane, which is the largest lake shown in the scene, Fig. 1. The scene is about 35 km in range and 70 km in azimuth. Kuortane area is relatively flat (40– 226 m), and areas of different land cover types are fragmented. There are a few built-up areas (called here urban), but these

3654


TABLE I S CENES AND P ROGRAMS U SED FOR C LASSIFICATION

mostly belong to discontinuous fabric of single-family houses. Built-up areas contain also some mature trees. Kuortane represents typical Finnish, conifer-dominated mixed forestland. Coniferous forest on mineral soil is the dominating forest type. Second type is mixed (contains both coniferous and deciduous trees) forest on mineral soil. Forest stands are often long and narrow, in some cases, only 25-m wide. The dominant soil type is glacial drift, but sand areas also exist. The main tree species are: 1) Norway spruce (Picea abies); 2) Scots pine (Pinus sylvestris); and 3) birch (Betula pendula and Betula pubescens). Some peat land areas are used for peat harvesting. The area of Kuortane covered by one PALSAR scene is shown in Fig. 1. A Pauli color-coded presentation [1] of polarimetric data is used. In this presentation, power scattered by single or odd-bounce targets (HH+VV) is represented by blue color, double-bounce (HH-VV) by red, and volume scattering (HV) by green. Blue color (surface scattering) can be found in areas containing, for example, field or water, red and white in built up areas, and green in vegetated areas. B. ALOS PALSAR Data ALOS PALSAR is a spaceborne fully polarimetric SAR operating at L-band [17], [18]. Three fully polarimetric PALSAR scenes and one dual-polarized scene are used in this paper. The pixel spacing of the ortho-rectified data is 25 m, ground range resolution is 28.3 m, and azimuth resolution is 4.6 m. For the dual polarization scene, the pixel spacing is 12.5 m, ground range resolution is 18.4 m, azimuth resolution is 4.6 m, and the size of the scene is 50 km × 50 km. The scenes and their specifications are gathered into Table I. The fully polarimetric scenes used in this experiment were acquired in November 2006, March 2007, and May 2007. All ALOS PALSAR data are single look complex (SLC) data. During the November acquisition, some snow was on the ground, but the water surfaces were mostly open. According to Weather Underground service (www.wunderground.com), the temperature was around 0 ◦ C (measured in Kauhava/EFKA airport, 42 km away from Kuortane) and it was snowing lightly. In the March scene, the lakes were covered with ice and some floating water on top. The temperature was 6.0 ◦ C and the day was clear. In the May scene, spring was already progressing in full speed, the temperature at the time of acquisition was 11 ◦ C and it was raining lightly. In the multitemporal case, all these three scenes were used together. One dual-polarized scene acquired in June 2007 is used to compare results obtained in different operation modes of ALOS. The polarizations used are HH and HV. During the acquisition, it was raining and the temperature was 12 ◦ C.

Fig. 2.

Data types and the analysis which was performed.

Fully polarimetric data are used in several ways in this study. The combinations of data and analysis methods are shown schematically in Fig. 2. C. CORINE Land Cover 2000 Corine Land Cover 2000 (CLC2000) [19] is used to verify classification results and label clusters of unsupervised classification. The land cover database of Finland was produced by Finnish Environment Institute (SYKE) [20]. It is based on numerical interpretation of optical satellite images and data integration with existing digital map. In the whole Finland, 44 classes were used. The resolution of the land cover data is 25 m. The CLC data, with minimum mapping unit of 25 hectares, was generalized from this national database. The national part of the database is used in this paper. For the purpose of this study, Corine classes are combined to generalized classes: water, field, sparse forest, dense forest, peat land, and urban areas (Fig. 3). When using five classes, pixels belonging to class urban are combined with class dense forest, since the discontinuous urban fabric contained a lot of trees and therefore is mixed with forest. When going down to four classes, forest classes are combined to one class named forest. In the case of three classes, peat land is combined with field areas, and the class has been named open. D. Preprocessing Ortho-rectified PALSAR scenes are used for classification. The preprocessing followed the procedure described in [21]. A digital elevation model obtained from National Land Survey of Finland has been used in the ortho-rectification process. The


3655

TABLE II N UMBER OF T RAINING P IXELS U SED IN THE S UPERVISED C LASSIFICATION (T OTAL 2.3 M EGAPIXELS )

Fig. 3. Reference data of the six classes used in the experiment. The classes have been combined from CLC 2000 classes. Blue = water, dark green = dense forest, light green = sparse forest, yellow = field, red = urban, and orange = peat land. CLC 2000 land cover (25 m): SYKE (partly MMM, MML, VRK).

DEM has been calculated from the contour lines and coastline elements of the basic map and it has a pixel spacing of 25 m [22]. The contour lines are 5-m apart so vertical accuracy can be estimated to be about 2.5 m. Polarimetric data are averaged (as Stokes matrices) over six consecutive lines before ortho-rectification. This results in data where the pixel spacing approximately corresponds that of the DEM. For the dualpolarized scene, the digital elevation model is first resampled to 12.5-m pixel. The dual-polarized scene has been preaveraged over five lines. Map-derived ground control points are used to revise the geo-location computed from the state vector and time code data. Topography-induced variations in scattering area are corrected (radiometric correction) in connection with orthorectification by normalizing the values with respect to projected pixel area [21]. E. Classification Methods and Data Used Methods and scenes used for classification are shown in Table I. Methods and the data used in the experiments are explained in the following sections. Data types are also summarized in Fig. 2. Number of training pixels used in the supervised classifications with six, five, four, and three classes is listed in Table II. Pixels for urban are used only with six classes, dense and sparse forest are combined when using four classes, and field and peatland when using three classes. Fully Polarimetric Data: PolSARpro (provided by ESA, [23]) is used to classify fully polarimetric data. The data are

read having the information that it is six-look, and coherency matrices (T3) are formed. Before classification, Lee filter [29], which is built in PolSARpro, is used for speckle filtering with window size of three. In Lee filtering, the elements of the covariance matrix are filtered by averaging the covariance matrix of neighboring pixels. An edge-aligned nonsquare window and the local statistics filter are used. In unsupervised classification, the clustering is done using Wishart H/A/α classification [6]. In this segmentation scheme, different clusters are initialized using the results of H/A/α decomposition. A maximum likelihood (ML) statistical clustering is done on the polarimetric data sets based on the complex Wishart probability density function. This procedure produces 16 segments in PolSARpro. For unsupervised PolSARpro experiments, the image is replicated to fill the whole area and masked back to original dimensions after the classification, since PolSARpro does not accept missing pixel values on the sides of the rectified image (see Fig. 1). To assign the segments to land cover classes, a confusion matrix against the Corine 2000 data is calculated for all three data sets. The percentage of water, field, sparse forest, dense forest, peat land and urban areas inside the segments are calculated and the segment are assigned to the dominating class. In supervised classification, training areas are defined manually for each class from Pauli color-coded presentation. Then, the classifier calculates the Wishart statistics of the training areas and then assigns each pixel to the closest class using maximum likelihood decision rule. Class urban is not used in supervised classification of fully polarimetric data. The accuracy of the classification is evaluated against the Corine data. Intensity Data: AutoChange is an unsupervised change detection and recognition system for forestry, which was developed at VTT [30]. It can also be used for general classification purposes. In AutoChange, the k-means algorithm is used for clustering. First, from the input feature images, homogeneous pixel groups are selected. The criterion for homogeneity is either the standard deviation or the coefficient of variation within the pixel group as compared to corresponding statistics of the whole image. The selected observations are clustered into a predefined number of clusters using k-means algorithm. All the pixels in the feature image are then classified into the obtained clusters using minimum distance classifier (Euclidean distance). The image is scaled in such a way that the weight of each channel is similar. Output data are given in the original intensity values. ER Mapper [31] is used for supervised classification. The training areas for classes are defined from Pauli color-coded presentation of the data. According to the statistics of the

3656


TABLE III F EATURES C ONSIDERED IN THE S TUDY. I NDICATES S PATIAL AVERAGING (M ULTILOOKING ), S I S THE S CATTERING M ATRIX, R I S THE S TOKES V ECTOR . A PPLICATION D OMAINS OF THE F EATURES IN THE R EFERENCE P UBLICATIONS A RE G IVEN

training areas, the pixels are assigned to the selected classes. A pixel is assigned to a class by taking into account the distance weighed by the covariance matrix of the means, and the prior probability the pixel belongs to it. Here, prior probabilities have been defined equal. Since only intensity data can be used for clustering in AutoChange and ER Mapper, intensities of five channels are used; 1) HH; 2) HV; 3) VV; 4) HH+VV; and 5) HH-VV. Arithmetical operations in sum and difference channels are done in complex domain. Absolute values of all these channels are used in this paper though absolute value signs are not shown. The channels were selected due to their expected impact on the classification. The sum represents single or odd-scattering part, difference indicates a scattering mechanism characterized by double-bounce, and HV characterizes volume scattering according to Pauli decomposition [1]. Forest canopy produces volume scattering so HV can be used as an indication of the biomass of a pixel. AutoChange is used for the multitemporal analysis. All the data was stored into a single file and processed using equal weight on each channel.

Fig. 4. Classified map of fully polarimetric data, which was segmented with unsupervised H/A/α Wishart classification and labeled to three classes (left) and Corine 2000 land cover map of the same area (right). Classes open, forest, and water were used in the classification. CLC2000 land cover (25 m): SYKE (partly MMM, MML, VRK).


3657

TABLE IV C ONFUSION M ATRIXES OF THE C LASSIFICATIONS W ITH D IFFERENT F EATURES /M ETHODS . F ULLY P OLARIMETRIC DATA S ET ACQUIRED IN M AY 2007 WAS U SED , IF NOT M ENTIONED OTHERWISE . S PARSE F OREST S HORTENED TO S PARSE , D ENSE F OREST TO F OREST, AND P EAT L AND TO P EAT. (a) U NSUPERVISED W ISHART C LASSIFICATION. (b) S UPERVISED W ISHART C LASSIFICATION. (c) AUTO C HANGE : I NTENSITY C HANNELS HH, HV, VV, HH+VV, HH-VV. (d) ER MAPPER : S UPERVISED C LASSIFICATION W ITH I NTENSITY C HANNELS HH, HV, VV, HH+VV, HH-VV. (e) AUTOCHANGE : I NTENSITY C HANNELS HH, HV. (f) AUTOCHANGE : D UAL -P OLARIZED . R ESULTS F ROM J UNE 2007. 0 , H, A, α, AND δ. (h) AUTOCHANGE : M ULTITEMPORAL F EATURES : (g) AUTOCHANGE : F EATURES σHV N OVEMBER 2006, M ARCH 2007, AND M AY 2007

Polarimetric Features: Features are extracted from the Lee filtered fully polarimetric images by calculating the values pixel-by- pixel with equations presented in Table III. In this paper, only these features are called polarimetric features. 0 σHV can be used for example for biomass estimation so it can be expected to separate, for example, forest versus open field. H, A, and α are parts of a decomposition, which can be used to separate different scattering mechanisms: 1) surface scattering (open areas); 2) double bounce (houses and large trees); and 3) volume scattering (forest canopy). The depolarization ratio gives information on the scattering mechanisms and increases

contrast in some cases. All together, these features can be expected to give information and separation between different types of land cover. Statistical analysis called stepwise discriminant analysis is used to help select the features, which are later used in the classification. A stratified sample of 32 000 pixels (out of a bit more than 2 million pixels) was taken of the six land cover classes included in the analysis (on the basis of CORINE data). These classes are: urban, field, sparse forest, forest, peat land, and water. The analysis is made with statistical analysis package SPSS v. 14.0 [32]. Ideally, the features should fulfill the

3658


underlying assumptions of the discriminant analysis. These assumptions include the following: 1) normality of the distribution; 2) homogeneity of variance/covariance; 3) randomness of samples; and 4) correctness of the initial classification. The effect of unequal variances is reduced because group sizes are approximately equal. A random sample of the original data is taken for the analysis. The initial classification comes from Corine 2000, and it can be assumed to be correct. The images to be classified have been multilooked and filtered, i.e., the features have been averaged before processing and Q-Q-plots were used to check the normality of the distributions of the variables. Even though the distributions of the variables do not ideally follow normal distribution, it was decided to analyze the variables with stepwise discriminant analysis and use the result as guidance in the variable selection process. Selecting a better method for variable selection is an issue of further study. The actual classification methods used in this paper do not require the variables to be normally distributed. Stepwise discriminant analysis builds a predictive model for group membership as a linear model of the predictor values. At each step, the variable that minimizes the overall Wilks’ lambda is entered to the model. The analysis was run for all the features and the average step of each feature entering the model was calculated based on the three images (November, March, and May). This result, together with physical meaning of the features, was used for the selection. After the features were selected, AutoChange is used for classification of the data. Multitemporal analysis is done with AutoChange, the same way as with intensity data. III. R ESULTS A. Classification Using Fully Polarimetric Data Clustering was done using unsupervised H/A/α Wishart classification and the classes were labeled according to Corine. In Fig. 4, a sample map of classification result with three classes (open, forest and water), and Corine 2000 land cover of the same area can be seen. An example of the confusion matrix can be seen in Table IV(a). Water is mixing with field areas probably due to seasonal effects. As an example of results obtained using supervised Wishart classification, confusion matrix of the May data set is shown in Table IV(b). Here, mixing happens mostly between sparse and dense forest and peatland and field. Classification result with five classes can be seen in Fig. 5.

B. Classification Using Intensity Data Unsupervised classification was done using channels HH, HV, VV, HH+VV, and HH-VV. To be sure that also small classes are represented by clusters, it was decided to use 100 clusters for the clustering. They were assigned to classes the same way as unsupervised H/A/α Wishart classification results using the CORINE 2000 land cover map. The location of the clusters in the HV versus HH+VV plane can be seen in Fig. 6. Clearly, HV axis is proportional to the biomass content. The HH+VV channel differentiated, for example, peat land from other classes. From a similar figure with HH-VV on y-axis, it can be concluded that HH-VV was somewhat effective

Fig. 5. Result of supervised Wishart classification, May 2007 data. Blue = water, dark green = dense forest, light green = sparse forest, yellow = field, and orange = peat land.

in separating urban areas. Confusion matrix of the May data set can be seen in Table IV(c). As can be expected by looking at Fig. 6, there has been mixing between classes on the borders of the bubbles, for example, water and field, peatland and field, and sparse forest and dense forest. With three classes, worst confusion is between water and field, which might be explained by the image acquisition times when either nothing was growing in the fields or the growing season had just barely started. Same kind of mixing can also be seen when using supervised classification [Table IV(d)]. With supervised classification of fully polarimetric data, there is less mixing; even though the training areas are the same, so with polarimetric data, it is easier to separate these classes. Comparison With Dual Polarization Results: To compare classification of dual-polarized and fully polarized data, three classifications were done. Unsupervised classification with AutoChange was done using only two intensity channels, HH and HV of the May scene [Table IV(e)]. Then the classification was done on the actual dual-polarized data from June 2007. Unsupervised H/A/α Wishart classification was used for comparison for the fully polarimetric data of the May scene. The results can be seen in Table IV(a), (e), and (f). In this evaluation, there seems to be no significant difference between the results. The higher resolution dual-polarized data gave slightly better results than lower resolution fully polarimetric data. It was better in classifying water areas. With intensities of channels HH and HV, it was not possible to separate the urban class. The classification accuracy was 3% and 1% lower than for actual dual-polarized data and fully polarimetric data, respectively.


3659

Fig. 6. Clustering result of November data set with 100 classes. Different type of land covers are divided into different classes according to CORINE data. Blue = water, yellow = field, orange = contains peat land, lightgreen = sparse forest, green = dense forest, red stripes = contains urban areas.

Fig. 7. Comparison of overall classification accuracy with different data types in different seasons when using classes water, forest, sparse forest, field, and peat land. TABLE V N UMBER OF C LASSES W HICH C OULD B E S EPARATED

Fig. 8. Comparison of overall classification accuracy with different data types. May 2007 data set except dual-polarized June 2007. Multitemporal bars include data from November 2006, March 2007, and May 2007.

C. Classification of Polarimetric Features Selected With SPSS

Due to this result, it can be expected that if the fully polarimetric data would have the same resolution as the dual-polarized data, fully polarimetric data would actually perform better than dualpolarized data. With the actual dual-polarized data, there were some urban clusters. The result with five classes is shown in Table IV(f) to ease the comparison with the results extracted from fully polarimetric data. The overall accuracy including the class urban is 65.6%, but the number of pixels belonging to that class is very small (0.03% of all the pixels), so no conclusions can be drawn from this accuracy.

According to the stepwise discriminant analysis, a combi0 nation of features σHV , H, A, α, and δ (depolarization ratio) gave the best results. The selection was not based on one image only, but on average of the three images available. The decision on selecting features was made on basis of the discriminant analysis and the physical meaning of the variables. Five best features were selected to minimize the number of features and still get reasonable classification accuracy. Adding more features did not increase the accuracy significantly (only 0.3% increase in overall accuracy when including the 6th best feature of the discriminant analysis). These selected polarimetric features were used for unsupervised classification with AutoChange. Confusion matrix can be seen in Table IV(g). Sparse and dense forest classes are mixing also with this data type.

3660


TABLE VI K APPA C OEFFICIENTS OF AGREEMENTS AND T HEIR VARIANCES FOR D IFFERENT C LASSIFICATIONS W ITH D IFFERENT N UMBER OF C LASSES (T HREE , F OUR , F IVE , OR S IX ) B EST VALUES H AVE B EEN H IGHLIGHTED

D. Comparison of Classification Results in Different Seasons In Fig. 7, the overall accuracy of different data types in three polarimetric scenes is compared with five classes, i.e., urban class is left out of the analysis. Methods using the whole polarimetric data performed better than the ones using only intensity data. It seems that the winter pictures (containing some wet snow) are more difficult to classify than the snow-free spring picture. In [33], it has been noted that in thawing conditions, the signatures of forest differ depending on temperature dynamics, snow-cover properties, and precipitation. About half of the test area is covered with forest so the effects can be seen also in these results. In winter scenes, only unsupervised classification of the selected polarimetric features had classification accuracies and number of separable classes at the same level as with spring images. The number of classes which can be separated (labeling classification result according to CORINE) can be seen in Table V. Training areas for classes water, field, peat land, sparse, and dense forest were imported from ER Mapper to PolSARpro. Due to limitations of PolSARpro or the data, it was not possible to import urban class. Unsupervised classification with polarimetric features had the best performance if both the number of separable classes and overall classification accuracy was taken into account. Overall, the differences of classification accuracies of images acquired in different seasons seemed to be small.

E. Comparison of Classification Results With Different Features Comparison of the overall accuracies (vertical axis) between methods (horizontal axis) and different number of classes (three, four, five, six) for May data can be seen in Fig. 8. Kappa coefficients of agreement and their variances for the same classifications can be seen in Table VI. They have been calculated the same way as in [13]. Classification using fully polarimetric data (Fully pol. unsup. and Fully pol. sup. in Fig. 8) gave better results than intensity data only. The difference was 5% maximum when using three to five classes. Supervised classification performed slightly better than unsupervised classification (max. of 2.8%). Additional column Dual pol. unsup. was added to include also actual higher resolution dual-polarized data into the comparison, even though it has not been acquired at the same time as the polarimetric data (summer versus spring data). Also, the multitemporal results with unsupervised classification of intensity data and multitemporal polarimetric

features were added to the same figure so that the reader can estimate the benefits of using multitemporal analysis in case of PALSAR data. Dual-polarized (HH, HV) data extracted from a fully polarimetric data set performed well when compared with intensitydata-only results. The difference was −1.3% maximum and less than −6% compared to classification results with fully polarimetric data. When the actual higher resolution dualpolarized data were used, the accuracy was almost as good as with fully polarimetric supervised classification. Actual dualpolarized data have higher resolution (18 m versus 28 m) and higher incidence angle (39◦ versus 24◦ ) than fully polarimetric or extracted dual-polarized data. This mainly explains the difference in results between the actual dual-polarized (HH, HV) data and extracted HH and HV channels. Also, different phase of growing season (end of May versus end of June) can have some effect on the results. There was no significant difference between the classification accuracies of polarimetric features or fully polarimetric data with unsupervised classification. Classification using features gave slightly better results (2.2% minimum) than the basic intensity channels HH, HV, VV, HH+VV, and HH-VV. In the November data set, classification with features performed better than the other methods; the classification accuracy with five or six classes was as good as with May data. With other data types, it was either not possible to separate five or six classes or the accuracy was considerably lower (9%–17%). Multitemporal analysis seems to give about 2% better results than classification based on one data set only. Multitemporal analysis was not done on the fully polarimetric data (not possible with PolSARpro v3.0 beta 1/2). Kappa coefficients of agreement agree with the overall accuracies and the same data types perform well with both meters. Kappa coefficients of agreement are moderate for all the classifications. The classifications were significantly better than a random result with 95% confidence level. With the same confidence level, the methods were different in pairwise testing, except that when using five classes, it was not possible to differentiate between unsupervised classification of polarimetric features and fully polarimetric supervised classification. IV. C ONCLUSION Fully polarimetric data from a new instrument, ALOS PALSAR, were used to compare different data types in the application area of land cover mapping. Different methods were


used to compare data from different perspectives. Both fully polarimetric and intensity data drawn from the polarimetric data were used. Fully polarimetric data gave better results than intensity data only. With polarimetric features, it was possible to obtain classification accuracy of the same level as with fully polarimetric analysis. Supervised classification improved results compared to unsupervised classification with intensity data, but with fully polarimetric data, there was no significant difference. This might suggest that in case of polarimetric data, unsupervised classification would be preferable since it allows more automated classification procedure. Urban areas were difficult to classify with all the methods tested here. This is in line with previous results [9]. Urban class covered only 2.9% of the pixels of the test site so the whole class could have been left out from the analysis as was done in Dobson et al. [10]. In this paper, they were used for testing purposes, but later in the analysis, combined with class dense forest (both in reference data and image data) as in Table IV and Fig. 7. Segregation between three classes, open, forest, and water was possible with relatively good, over 80% accuracy for all the data types. Peat land areas were mixed with forest and field. Most of the peat land areas also had some tree cover, which explains mixing with forest class. Flat areas with lowwater content were easily mixed with field areas. Separation between sparse and dense forest was difficult, which might be due to their definition which is not related to radar directly. Forest area is defined as forest if crown cover > 30% and tree height > 5 m, otherwise it is defined as sparse forest. In the future, temporal variability could be used to improve classification accuracy for multitemporal data as was done in Bruzzone et al. [14] and Häme et al. [16]. Also, long-term coherence could be used if interferometric data are available. This could improve particularly the separability of urban class, since it has low temporal variability. Fully polarimetric data is preferable compared to intensity data since it gives better results particularly in winter scenes (5% difference in overall accuracy in November 2006 data set) and also it can be used to extract polarimetric features, which can then be used for classification. Extracted polarimetric features give the possibility to use tools which were not developed for the analysis of polarimetric data and still include some benefits of polarimetric data in the analysis. Supervised classification can be expected to perform better than unsupervised classification, given that the training areas can be selected accurately. Unsupervised classification gives also reasonable results. If multitemporal data are available, it can be used to improve the classification result. Dual polarization data are an attractive alternative in cases where fully polarimetric data are not available or its resolution is lower than the resolution of dual-polarized data. In the case of ALOS PALSAR, where the resolution and incidence angle are higher for dual-polarized than fully polarimetric data, the performance is about the same as with fully polarimetric data. ACKNOWLEDGMENT ALOS/PALSAR data were provided by ESA in the context of ALOS/Aden AO project AOALO.3713. Data from following resources have been used in the production of CLC2000 material: SYKE, MML, MMM (fields 1999), and VRK (built-up

3661

areas 2001). Material from Metsähallitus and UPM Kymmene has been utilized in the interpretation of the satellite images. The first author would like to thank J. Kilpi for helping with revising the paper and discussions on statistics. R EFERENCES [1] S. Cloude and E. Pottier, “A review of target decomposition theorems in radar polarimetry,” IEEE Trans. Geosci. Remote Sens., vol. 34, no. 2, pp. 498–518, Mar. 1996. [2] S. Cloude and E. Pottier, “An entropy based classification scheme for land applications of polarimetric SAR,” IEEE Trans. Geosci. Remote Sens., vol. 35, no. 1, pp. 68–78, Jan. 1997. [3] J.-S. Lee, M. R. Grunes, T. L. Ainsworth, L.-J. Du, D. L. Schuler, and S. R. Cloude, “Unsupervised classification using polarimetric decomposition and the complex Wishart classifier,” IEEE Trans. Geosci. Remote Sens., vol. 37, no. 5, pp. 2249–2258, Sep. 1999. [4] L. Ferro-Famil, E. Pottier, and J.-S. Lee, “Unsupervised classification of multifrequency and fully polarimetric SAR images based on the H/A/Alpha–Wishart classifier,” IEEE Trans. Geosci. Remote Sens., vol. 39, no. 11, pp. 2332–2342, Nov. 2001. [5] J.-S. Lee, M. R. Grunes, and E. Pottier, “Quantitative comparison of classification capability: Fully polarimetric versus dual and singlepolarization SAR,” IEEE Trans. Geosci. Remote Sens., vol. 39, no. 11, pp. 2343–2351, Nov. 2001. [6] J. S. Lee, M. R. Grunes, E. Pottier, and L. Ferro-Famil, “Unsupervised terrain classification preserving polarimetric scattering characteristics,” IEEE Trans. Geosci. Remote Sens., vol. 42, no. 4, pp. 722–731, Apr. 2004. [7] A. Freeman and S. L. Durden, “A three-component scattering model for polarimetric SAR data,” IEEE Trans. Geosci. Remote Sens., vol. 36, no. 3, pp. 963–973, May 1998. [8] P. Lumsdon, S. R. Cloude, and G. Wright, “Polarimetric classification of land cover for Glen Affric radar project,” Proc. Inst. Elect. Eng.—Radar, Sonar Navig., vol. 152, no. 6, pp. 404–412, Dec. 2005. [9] H. Skriver, J. Schon, and W. Dierking, “Land-cover mapping using multitemporal, dual-frequency polarimetric SAR data,” in Proc. IEEE IGARSS, Jul. 2000, vol. 1, pp. 331–333. [10] M. C. Dobson, L. E. Pierce, and F. T. Ulaby, “Knowledge-based landcover classification using ERS-1/JERS-1 SAR composites,” IEEE Trans. Geosci. Remote Sens., vol. 34, no. 1, pp. 83–99, Jan. 1996. [11] L. E. Pierce, F. T. Ulaby, K. Sarabandi, and M. C. Dobson, “Knowledgebased classification of polarimetric SAR images,” IEEE Trans. Geosci. Remote Sens., vol. 32, no. 5, pp. 1081–1086, Sep. 1994. [12] D. H. Hoekman and M. J. Quinones, “Biophysical forest type characterization in the Colombian Amazon by airborne polarimetric SAR,” IEEE Trans. Geosci. Remote Sens., vol. 40, no. 6, pp. 1288–1300, Jun. 2002. [13] C. da Costa Freitas, L. de Souza Soler, S. J. S. Sant’Anna, L. V. Dutra, J. R. dos Santos, J. C. Mura, and A. H. Correia, “Land use and land cover mapping in the Brazilian Amazon using polarimetric airborne P-band SAR data,” IEEE Trans. Geosci. Remote Sens., vol. 46, no. 10, pt. 1, pp. 2956–2970, Oct. 2008. [14] L. Bruzzone, M. Marconcini, U. Wegmüller, and A. Wiesmann, “An advanced system for the automatic classification of multitemporal SAR images,” IEEE Trans. Geosci. Remote Sens., vol. 42, no. 6, pp. 1321– 1334, Jun. 2004. [15] Y. Rauste, T. Häme, H. Ahola, N. Stach, and J.-B. Henry, “Detection of forest changes over French Guiana using ERS-1 and ASAR imagery,” presented at the Envisat Symp., Montreux, Switzerland, Apr. 23–27, 2007. [16] T. Häme, Y. Rauste, S. Väätäinen, H. Ahola, N. Stach, and A. Salvado, “Monitoring forest cover in french guiana using space-borne radar data,” presented at Forests Remote Sens.: Methods Oper. Tools (Forestsat), Montpellier, France, Nov. 5–7, 2007. [17] A. Rosenqvist, M. Shimada, N. Ito, and M. Watanabe, “ALOS PALSAR: A pathfinder mission for global-scale monitoring of the environment,” IEEE Trans. Geosci. Remote Sens., vol. 45, no. 11, pt. 1, pp. 3307–3316, Nov. 2007. [18] Jaxa, 2007. [Online]. Available: http://www.eorc.jaxa.jp/ALOS/about/ palsar.htm [19] [Online]. Available: http://www.eea.europa.eu/themes/landuse/clcdownload [20] P. Härmä, R. Teiniranta, M. Törmä, R. Repo, E. Järvenpää, and M. Kallio, “Production of CORINE2000 land cover data using calibrated LANDSAT 7 ETM satellite image mosaics and digital maps in Finland,” in Proc. IEEE Int. Geosci. Remote Sens. Symp., Anchorage, AK, Sep. 20–24, 2004, vol. 4, pp. 2703–2706.

3662


[21] Y. Rauste, A. Lönnqvist, M. Molinier, J.-B. Henry, and T. Häme, “Orthorectification and terrain correction of polarimetric SAR data applied in the ALOS/Palsar context,” in Proc. IEEE IGARSS, Jul. 2007, pp. 1618–1621. [22] [Online]. Available: http://www.maanmittauslaitos.fi/en/default.asp? id=494 [23] E. Pottier, L. Ferro-Famil, S. Allain, S. Cloude, I. Hajnsek, K. Papathanassiou, A. Moreira, M. Williams, T. Pearson, and Y.-L. Desnos, “An overview of the PolSARpro v2.0 software. The educational toolbox for polarimetric and interferometric polarimetric SAR data processing,” presented at the POLinSAR ESA Workshop, Frascati, Italy, Jan. 2007. [24] H. Skriver, W. Dierking, P. Gudmandsen, T. Le Toan, A. Moreira, K. Papathanassiou, and S. Quegan, “Applications of synthetic aperture radar polarimetry,” in Proc. Workshop Appl. SAR Polarimetry Polarimetric Interferometry (POLinSAR), Jan. 2003, pp. 11–16. [25] S. Quegan, T. Le Toan, H. Skriver, J. Gomez-Dans, M. C. GonzalezSampedro, and D. H. Hoekman, “Crop classification with multitemporal polarimetric SAR data,” in Proc. Workshop Appl. SAR Polarimetry Polarimetric Interferometry (POLinSAR), Jan. 2003, p. 9.1. [26] W. Dierking, H. Skriver, and P. Gudmandsen, “SAR polarimetry for sea ice classification,” in Proc. Workshop Appl. SAR Polarimetry Polarimetric Interferometry (POLinSAR), Jan. 2003, pp. 109–118. [27] J. R. Buckley, “Environmental change detection in prairie landscapes with simulated RADARSAT 2 imagery,” in Proc. IEEE IGARSS, Jun. 2002, pp. 3255–3257. [28] R. Touzi, S. Goze, T. Le Toan, A. Lopes, and E. Mougin, “Polarimetric discriminators for SAR images,” IEEE Trans. Geosci. Remote Sens., vol. 30, no. 5, pp. 973–980, Sep. 1992. [29] J. S. Lee, M. R. Grunes, and G. de Grandi, “Polarimetric SAR speckle filtering and its implication for classification,” IEEE Trans. Geosci. Remote Sens., vol. 37, no. 5, pt. 2, pp. 2363–2373, Sep. 1999. [30] T. Häme, I. Heiler, and J. San Miguel-Ayanz, “An unsupervised change detection and recognition system for forestry,” Int. J. Remote Sens., vol. 19, no. 6, pp. 1079–1099, Apr. 1998. [31] ER Mapper, 2007. [Online]. Available: http://www.ermapper.com/ [32] SPSS for Windows, SPSS Inc., Chicago, IL, 2005, Rel. 14.0.1. [33] M. Santoro, J. E. S. Fransson, L. E. B. Eriksson, M. Magnusson, L. M. H. Ulander, and H. Olsson, “Signatures of ALOS PALSAR L-band backscatter in Swedish forest,” IEEE Trans. Geosci. Remote Sens., vol. 47, no. 12, pp. 4001–4019, Dec. 2009.

Anne Lönnqvist was born in Somero, Finland, in 1977. She received the Master of Science (Tech.) (with honors), Licentiate of Science (Tech.), and Doctor of Science (Tech.) degrees in electrical engineering from the Helsinki University of Technology (TKK), Espoo, Finland, in 2001, 2004, and 2006, respectively. From 2000 to 2007, she was a Research Engineer at the TKK Radio Laboratory, where she was involved with submillimeter-wave antenna testing for the European Space Agency (ESA). Additionally, she was developing techniques for measuring radar cross section of scaled models of targets and reflectivity of radar-absorbing materials at submillimeterwavelengths. Since March 2007, she has been with the Remote Sensing Group, VTT Technical Research Centre of Finland, Espoo. Her current research interests include fully polarimetric SAR imagery.

Yrjö Rauste was born in Espoo, Finland, in 1956. He received the M.S. degree in surveying and mapping, the Licentiate of Technology, and the Doctor of Science (Tech.) degrees from the Helsinki University of Technology, Espoo, Finland in 1983, 1989, and 2006, respectively. Since 1979, he has been with VTT Technical Research Centre of Finland, Espoo, Finland except for visits to other research centers and his military service in 1983. From 1986 to 1987, he was a Visiting Scientist with the Institute for Image Processing and Computer Graphics, Graz Research Center, Graz, Austria. From 1997 to 1999, he was a Postdoctoral Grant Holder with the Joint Research Centre (JRC), European Commission, Ispra, Italy. From 1994 to 1996, he also served as the Secretary of the Finnish Society of Photogrammetry and Remote Sensing. He is currently a Senior Research Scientist with the Remote Sensing Group, VTT Technical Research Centre of Finland. His research interests include application of SAR image analysis and processing (particularly in forestry applications) and forest fire detection using optical satellite data.

Matthieu Molinier was born in France, in 1980. He received the Engineering degree from the Ecole Nationale Supérieure de Physique de Strasbourg, Strasbourg, France, in 2004, and the M.S. degree in image processing from Université Louis Pasteur, Strasbourg, France also in 2004. Since October 2004, he has been a Research Scientist at the VTT Technical Research Centre of Finland, Espoo, Finland, and a Postgraduate Student with the Laboratory of Computer and Information Science, Helsinki University of Technology, Espoo, Finland. His current research interests include change detection in satellite imagery, machine learning, video processing, and motion tracking.

Tuomas Häme received the M.S. and Ph.D. degrees from the University of Helsinki, Helsinki, Finland, in 1979 and 1992, respectively. Since 1979, he has been with the VTT Technical Research Centre of Finland, Espoo, Finland, where he is currently the Manager of the Remote Sensing Team, and has been a Research Professor on Earth observation since 2005. He was a Visiting Researcher at North Carolina State University, Raleigh, in 1989, and with the Joint Research Centre, Italy, from 1995 to 1996. He is an expert in remote sensing in forestry and has developed, particularly, methods for forest change detection, forest area mapping, and biomass estimation from local to continental extents. He has managed several international projects on Earth observation and is working in close cooperation with value-adding industry.