Polarimetric SAR Image Classification Using Multifeatures Combination and Extremely Randomized Clustering Forests

Terrain classification using polarimetric SAR imagery has been a very active research field over recent years. Although lots of features have been proposed and many classifiers have been employed, there are few works on comparing these features and their combination with different classifiers. In this paper, we firstly evaluate and compare different features for classifying polarimetric SAR imagery. Then, we propose two strategies for feature combination: manual selection according to heuristic rules and automatic combination based on a simple but efficient criterion. Finally, we introduce extremely randomized clustering forests (ERCFs) to polarimetric SAR image classification and compare it with other competitive classifiers. Experiments on ALOS PALSAR image validate the effectiveness of the feature combination strategies and also show that ERCFs achieves competitive performance with other widely used classifiers while costing much less training and testing time.

[1]  Yoav Freund,et al.  Game theory, on-line prediction and boosting , 1996, COLT '96.

[2]  Anthony Freeman,et al.  Fitting a Two-Component Scattering Model to Polarimetric SAR Data From Forests , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Jean-Claude Souyris,et al.  Use of the SVM Classification with Polarimetric SAR Data for Land Use Cartography , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.

[4]  J. Kong,et al.  Identification of Terrain Cover Using the Optimum Polarimetric Classifier , 2012 .

[5]  Thomas L. Ainsworth,et al.  Unsupervised classification using polarimetric decomposition and the complex Wishart classifier , 1999, IEEE Trans. Geosci. Remote. Sens..

[6]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[7]  Ridha Touzi,et al.  Target Scattering Decomposition in Terms of Roll-Invariant Target Parameters , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Eric Pottier,et al.  A review of target decomposition theorems in radar polarimetry , 1996, IEEE Trans. Geosci. Remote. Sens..

[9]  Thuy Le Toan,et al.  Polarimetric discriminators for SAR images , 1992, IEEE Trans. Geosci. Remote. Sens..

[10]  W. Dierking,et al.  SAR polarimetry for sea ice classification , 2003 .

[11]  Chih-Jen Lin,et al.  Combining SVMs with Various Feature Selection Strategies , 2006, Feature Extraction.

[12]  Frédéric Jurie,et al.  Randomized Clustering Forests for Image Classification , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Stephen L. Durden,et al.  A three-component scattering model for polarimetric SAR data , 1998, IEEE Trans. Geosci. Remote. Sens..

[14]  Olga Veksler,et al.  Fast Approximate Energy Minimization via Graph Cuts , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Jian Yang,et al.  A novel supervised classification scheme based on Adaboost for Polarimetric SAR , 2008, 2008 9th International Conference on Signal Processing.

[16]  James M. Keller,et al.  A fuzzy K-nearest neighbor algorithm , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[17]  Ron Kwok,et al.  Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution , 1994 .

[18]  Jean Richard Huynen,et al.  Stokes matrix parameters and their interpretation in terms of physical target properties , 1990, Other Conferences.

[19]  Antonio Criminisi,et al.  Object Class Segmentation using Random Forests , 2008, BMVC.

[20]  Pat Langley,et al.  Selection of Relevant Features and Examples in Machine Learning , 1997, Artif. Intell..

[21]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[22]  M. Hellmann,et al.  Classification of full polarimetric SAR-data using artificial neural networks and fuzzy algorithms , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

[23]  Jorma Laaksonen,et al.  Detecting changes in polarimetric SAR data with content-based image retrieval , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[24]  Laurent Ferro-Famil,et al.  Unsupervised terrain classification preserving polarimetric scattering characteristics , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Pierre Geurts,et al.  Extremely randomized trees , 2006, Machine Learning.

[26]  E. Pottier,et al.  On radar polarization target decomposition theorems with application to target classification, by using neural network method , 1991 .

[27]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[28]  Eric Pottier,et al.  An entropy based classification scheme for land applications of polarimetric SAR , 1997, IEEE Trans. Geosci. Remote. Sens..

[29]  Joseph R. Buckley Environmental change detection in prairie landscapes with simulated Radarsat 2 imagery , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[30]  Marc Acheroy,et al.  Fusion of PolSAR and PolInSAR data for land cover classification , 2009, Int. J. Appl. Earth Obs. Geoinformation.

[31]  Thuy Le Toan,et al.  Applications of Synthetic Aperture Radar Polarimetry , 2003 .

[32]  Y. Yamaguchi,et al.  CS-1-4 Four-Component Scattering Model for Polarimetric SAR Image Decomposition based on Covariance Matrix(CS-1. 電磁波計測・イメージングと波動情報処理技術, エレクトロニクス1) , 2005 .

[33]  S. Fukuda,et al.  Support vector machine classification of land cover: application to polarimetric SAR data , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[34]  Jian Yang,et al.  The boosting algorithm with application to polarimetric SAR image classification , 2007, 2007 1st Asian and Pacific Conference on Synthetic Aperture Radar.

[35]  Dengxin Dai,et al.  Supervised land-cover classification of TerraSAR-X imagery over urban areas using extremely randomized clustering forests , 2009, 2009 Joint Urban Remote Sensing Event.

[36]  E. Krogager New decomposition of the radar target scattering matrix , 1990 .

[37]  J. Zyl,et al.  Unsupervised classification of scattering behavior using radar polarimetry data , 1989 .

[38]  Thuy Le Toan,et al.  Crop classification with multitemporal polarimetric SAR data , 2003 .

[39]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.