Decision Fusion of Classifiers for Multifrequency PolSAR and Optical Data Classification

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1 Decision Fusion of Classifiers for Multifrequency PolSAR and Optical Data Classification N. Gökhan Kasapoğlu and Torbjørn Eltoft Dept. of Physics and Technology University of Tromsø Tromsø, Norway Abstract Forest detection and classification in tropical regions is very important for climate change research. Combining available data from different sensors is widely used in remote sensing to improve detection and classification performance. In this study, a decision fusion strategy is proposed to integrate optical and multifrequency PolSAR data for classification of rural areas including forest. Developed decision fusion strategy was validated with testing and validation samples which were manually selected from the high resolution satellite imagery. A total of three different sensor-originated scenes acquired on May 2010 in the Northwest of Tanzania were used in forest detection and classification experiments. The results show that combining classifiers for combinations of different sensororiginated features improves classification results for detailed class categories. Features which are properly modeled with the same statistical distribution are grouped and processed together. Classification results are weighted by using a reliability measure which is derived from confusion matrix of validation set. Therefore proposed decision fusion strategy improves the performance of parametric classifiers for some cases. Keywords Decision fusion; forest classification; multisensor data fusion; qualified majority voting (QMV); confusion matrix; precition I. INTRODUCTION Integrating all available data from different sensors to reach the most accurate and consistent results is not a trivial task. An increased number of satellite data is available today and there are various fusion strategies that have already been implemented in remote sensing. However the performance of fusion methods is strongly related to applications, data availability and features. A conventional way is to stack all available features in a vector for classification (i.e., stack vector approach, feature fusion) [1]. A classifier can be selected from parametric or nonparametric classifiers for stack vector approach. Neural networks (NNs), support vector machines (SVMs) are nonparametric classifiers and widely used for feature fusion [2, 3]. However there has been tendency to model data with a specific statistical distribution for parametric classifiers. This distribution can be Gaussian for optical data but this may not fit properly for synthetic aperture radar (SAR) data. The statistical model has been reported Gamma distribution for detected SAR products. Therefore assuming a specific distribution is valid model for all available data may not be reasonable for multisensor data fusion. Thus, in this study available features are grouped based on their sensor origin and every group is processed by an individual parametric classifier. Final decision is made by combining decisions of classifiers (class labels). Precision which is a decision reliability measure is derived from confusion matrix of validation set and is used to weight decisions [4]. The robustness of the final decision is strongly related to having various unbiased decisions. Therefore in this study, all possible combinations of grouped features are used in order to increase the number of decisions. Decisions are combined by using a consensual rule in decision fusion. An important concern about evaluation of decisions is assessing proper weights to them. Both consensual rule and validation samples are important for proper fusion process. Therefore validation samples which are not used in training process are selected from available samples. In this study class specific decision reliability (CSDR) measure, precision, were used instead of general accuracy measures such as overall accuracy (OAA). The proposed data fusion strategy improves final classification results for detailed class categories which include forested and deforested regions as well as cultivated lands and different types of soils for rural area land cover classification. II. STUDY AREA AND DATASETS Our study area is mostly a rural area in the Northwest of Tanzania and covers forest and deforested regions including cultivated lands with different types of soil and bare areas. Forest is categorized based on tree density. Open trees, very open tress, trees and scrubs are such detailed class categories that is associated with general forest class. Cultivated lands have different dense of vegetation and soil types. Detailed categorization of the cultivated lands can be done based on reflected solar radiations in visible bands as follows: Cultivated lands white, brown and green. Rise fields is another detailed class category in general cultivated lands class and precisely detected in C band SAR images (e.g., Radarsat 2). Forest and deforested regions can be easily detected in L band SAR images (e.g., ALOS PALSAR). Multispectral sensors (e.g., Landsat 7 ETM+) with visible and near infrared (NIR) channels have advantages to classify detailed class categories such as forests with different tree densities, vegetation and soil types /13/$ IEEE 411

2 (a) (b) (c) (d) Fig. 1. (a) Study site: The Northwest of Tanzania. (b) Radarsat-2 color composite image. (c) ALOS PALSAR color composite image. (d) Landsat-7 ETM+ color composite image. TABLE I. Class ID DETAILED CLASS CATEGORIES Class Description 1 Bare areas 2 Bare soil 3 Cult. land-white 4 Cult. land-brown 5 Cult. land-red 6 Cult. Land - Rise 7 Grassland 8 Woody veg. (thicket) 9 Trees and shrubs 10 Very open trees 11 Open trees Returned signals from the surface of the earth can be affected by topography. Therefore topographic corrections are necessary and should be applied for both optical and polarimetric SAR (PolSAR) scenes. In our study site topography is mostly flat and reflected signals are less influenced by the topography. Radarsat 2, ALOS PALSAR and Landsat 7 ETM+ scenes acquired in May 2010 were used in decision fusion experiments. Study site and scenes used in the experiments are shown as pseudocolor images in Fig. 1 and detailed class categories in the study site are outlined in Table I. III. FEATURE EXTRACTION AND IMAGE PROCESSING The performance of features in classification process is application dependent. In this study, features which are originated from three different sensors were used in the classification and the decision fusion experiments. Quad Polarized Radarsat 2 (C band), dual polarized ALOS PALSAR (L band) and Landsat 7 ETM + scenes acquired in May 2010 were used in the experiments. Extracted features and their sensor origins are outlined in Table II. TABLE II. MULTISENSOR DATASETS AND EXTRACTED FEATURES F. ID Sensor Feature Identification 0 T3 - T11 Co-occurrence mean 1 T3 - T22 Co-occurrence mean 2 T3 - T33 Co-occurrence mean 3 Radarsat 2 5 May 2010 C3 Span 4 C3 Co-pol.ratio 5 C3 Co-pol. correlation coefficients 6 C3 Co to cross pol. ratio 7 C2 - C11 Co-occurrence mean 8 C2 - C12 Real Co-occurrence mean 9 ALOS C2 - C22 Co occurrence mean PALSAR May 2010 C2 Span 11 C2 Co to cross pol. ratio 12 C2 Co to cross correlation coef.. 13 TM Band 1 14 TM Band 2 15 Landsat 7 TM Band 3 ETM May 2010 TM Band 4 17 TM Band 5 18 TM Band 7 A. PolSAR Data Representation and feature extraction. The original PolSAR data is single look complex and in slant range geometry. The PolSAR features are extracted from fully polarimetric SAR data in monostatic backscattering case for a reciprocal target (i.e., S S, S is the scattering element of horizontal transmitting and vertical receiving polarization) in C data format as defined follows [5]:.k T S S 2. S S S S 2. S S 2S S 2. S S (1) S S 2. S S S S 412

3 TABLE III. DETAILED CLASS CATEGORIES PolSAR FEATURES Co-polarized ratio Co to cross polarized ratio Total power Co-polarized phase difference Co to cross polarized phase difference Co-polarized correlation coefficients Co to cross polarized correlation coefficients 2. where. denotes ensemble averaging, * denotes complex conjugation and T denotes transpose operation. is a 3D target vector and can be written as follows: S 2.S S. (2) A multilook complex covariance matrix can be calculated by averaging n neighboring pixels as follows:.k T (3).k T A partial polarimetric representation of multilook complex covariance matrix for the dual polarized SAR data can be written in C2 data format: k. k T S S S S S S S S (4) where is a 2D target vector and is written as follows: S S. (5) The coherency matrix in data format is defined by using the 3D Pauli target vector 2.. (6) then multilook coherency matrix can be calculated by averaging n neighboring pixels: T k P.k T P.k P. (7) The PolSAR features which are originated from data format for Radarsat 2 and the data format for ALOS PALSAR are outlined in Table III. Additionally the co-occurrence mean texture feature is extracted from diagonal elements of the T in order to measure the mean of the probabilities from grey level co-occurrence matrix (GLCM) [6]. The equations for calculating the GLCM Mean is given as follows: μ. T (8) where is the repetition probabilities of reference pixel level i and neighbor pixel level j, N is total number of grey levels. The GLCM contains repetition frequencies of pixel values for a specific direction and distance within the predefined window. The normalized GLCM represents repetition probabilities (i.e., ) of grey levels. Using all grey levels for calculating GLCM is very computationally expensive and usually not useful. Therefore, we have applied a coarser quantization (i.e., 32 levels - 5 bit quantization) to the pixel values (i.e., the diagonal elements of the matrix). The window size for calculating neighbouring pixel statistics was chosen as 5x5 pixels. The GLCM were calculated for distance between a reference and neighbour pixel distance of one. Features extracted from PolSAR data are geocoded and the geocoded PolSAR data have 30 m pixel spacing. 413

4 Fig. 3. A confusion matrix for binary classification Fig. 2. The flow graph of the decision fusion scheme. B. Landsat 7 ETM+ Data Processing Optical sensors have been used successfully for many years in forest monitoring. Landsat satellite series are the most preferred sensors with sufficient spectral and spatial resolutions and revisiting time. Data continuity and free data policy are some other factors that make Landsat series satellite such a standard data in forestry. In this study a Landsat 7 ETM+ scene which has close acquisition time to PolSAR scenes was chosen. However the scene is in SLC-off mode and half of the region of interest (ROI) is affected by gap lines. The gap lines are exist mostly deforested side of the ROI (right part, see Fig. 1(d)). USGS Phase-2 Gap-Fill algorithm [7] was applied to resolve this problem. Additionally the Landsat scene has some haze affected pixels which are mostly in forested regions in the scene. A haze optimized transformation (HOT) is performed to remove haze effects [8]. After all preprocessing steps were applied, a total of 6 Landsat 7 ETM+ bands from the visible to the infrared regions in the electromagnetic spectrum were used in the multisensor data fusion experiments. The Landsat data have 30 m pixel spacing. IV. DECISION FUSION Detection capabilities of sensors are strongly related to physical properties of targets. For instance C band SAR data is widely used for oceanographic applications such as sea ice analysis, wind retrievals, ship and oil spill detection. It has relatively less effective use on land monitoring. This might be related to scattering mechanisms of land targets in C band. However wetland monitoring is an important application of C band SAR. Moreover, C band SAR data has been successfully used for detection of rice fields. The penetration capability of electromagnetic waves is related to wavelengths. Therefore for forest monitoring, L band SAR data is widely and successfully used as available spaceborn data. Multispectral optical sensors have advantages on land applications especially for crop and forest monitoring. Multispectral bands from visible to infrared regions make detailed class assessments possible. However the detection performance may change from dry to wet seasons. Therefore complementary use of different sensororiginated data is essential for robust analysis. One way is doing that is to use decision fusion for integration. In statistical modeling, there is a tendency to model same sensor-originated features together with the same statistical distribution. Therefore in this study, features are grouped based on their sensor origin. Then maximum likelihood classifiers (MLCs) are utilized to make first level labeling. The final decision is given by combining individual decisions of MLCs by using a consensual rule. This rule can be majority voting (MV) or Qualified Majority Voting (QMV) [9]. The flow graph of proposed decision fusion scheme is depicted in Fig. 2. Let is the decision of the classifier j for sample, i is a class label (i=1,,m, m total number of class), n is total number of classifiers then sum of votes of decisions for class i is written as follows: w. δj d (9) where w is the reliability of decisions for class i, δj d x is the kronecker delta function, δ 0, 1, and final class labeling is done as follows: (10) 1 : (11) where is assessed label to a unlabeled sample,.). For MV, every classification results have equal weights (w 1, for, ). For QMV, confidence levels of decisions are different and need to be specified based on some criterion. Accuracies of independent samples from training can be used to assess confidence levels of decisions in QMV. For this purpose cross validation is another auxiliary method to obtain unbiased performance of decisions. However, some classifiers have been designed to maximize majority class accuracies to keep the overall classification accuracy high. Therefore, overall classification accuracies alone may not be a sufficient measure for some cases. In this study the confusion matrix of validation set is used to derive confidence levels of decisions. Instead of choosing one confidence level for all class decisions, a class specific decision reliability (CSDR) measure, precision is used. A confusion matrix is depicted in Fig. 3 for a binary 414

5 classification problem to define the precision. In this study class precisions are utilized as a CSDR measure and can be defined according to sample confusion matrix (see Fig. 3) as follows: (12). (13) V. RESULTS AND CONCLUSION The proposed decision fusion scheme was implemented with both MV and QMV consensual rules. The confusion matrix derived reliability measure, precision, were used in QMV structure as decision weights for every class decisions. The stack vector approach was implemented for all available features with MLC for comparison. The classification maps are depicted for decision fusion with QMV and MV consensual rules, and the stack vector approach with MLC in Fig. 4 (a), (b) and (c), respectively. For detailed analysis confusion matrices associated with the fusion experiments are shown for decision fusion with QMV and MV consensual rules, and the stack vector approach with MLC in Fig. 5 (a), (b) and (c), respectively. A total of 11 classes were used as detailed class categories for labeling. Approximately 4 % overall classification accuracy increment were obtained by using the precision as a reliability measure in QMV consensual rule than the stack vector approach with MLC. The improvement was up to 1 % for MV consensual rule. The proposed decision fusion scheme produced better results than the stack vector approach with MLC. As a CSDR measure the precision performed well in QMV consensual rule for detailed class categories. However the improvement of the MV consensual rule with equal weighted decisions was small. The reason may be existence of some biased and limited number of decisions (seven which is the number of all combinations of grouped features) for integration. ACKNOWLEDGMENT The authors would like to thank Yngvar Larsen (Norut) for his support on the SAR geocoding software (GSAR) and Stian Anfinsen (University of Tromsø) for the PolSAR scenes. REFERENCES [1] J. A. Richards and X. Jia, Remote Sensing Digital Image Analysis, 4th ed., pp , Springer-Verlag Berlin Heidelberg, [2] J.A. Benediktsson, P.H. Swain, O.K. Ersoy, "Neural Network Approaches Versus Statistical Methods In Classification Of Multisource Remote Sensing Data," IEEE Trans. Geosci. Remote Sens. vol.28, no.4, pp , Jul [3] B. Waske and J. A. Benediktsson, Fusion of support vector machines for classification of multisensor data, IEEE Trans. Geosci. Remote Sens., vol. 45, no. 12, pp , Dec [4] N.G. Kasapoğlu, S.N. Anfinsen and T. Eltoft: Fusion of optical and multifrequency PolSAR data for forest classification, Proc. IGARSS 2012, Munich, Germany, pp , July, [5] J.S. Lee and E. Pottier, Polarimetric radar imaging : from basics to applications,crc Press, Taylor & Francis Group, [6] R.M. Haralick, K. Shanmugam and I. Dinstein, Textural features for image classification, IEEE Trans. Systems, Man and Cybernetics, vol. 3, no.6, pp , Nov [7] USGS Phase 2 gap-fill algorithm: SLC-off gap-filled products gap-fill algorithm methodology, (2004). (Available online at (accessed 30 Mart 2013)). [8] Y Zhang, B Guindon, J Cihlar, An image transform to characterize and compensate for spatial variations in thin cloud contamination of Landsat images, Remote Sensing of Environment, vol. 82, no. 2 3, pp , Oct [9] L. O. Jimenez, A. M. Morell, and A. Creus, Classification of hyperdimensional data based on feature and decision fusion approaches using projection pursuit, majority voting, and neural networks, IEEE Trans. Geosci. Remote Sens., vol. 37, no. 3, pp , May 1999, pp (a) (b) (c) Fig. 4. (a) Decision fusion result based on the QMV consencual rule. (b) Decision fusion result based on MV consencual rule. (c) MLC result with the stack vector. 415

6 (a) (b) (c) Fig. 5. (a) Confusion matrix of the QMV consencual rule based decision fusion. (b) Confusion matrix of the MV consencual rule based decision fusion. (c) Confusion matrix of the stack vector with MLC. 416