HIERARCHICAL CLASSIFICATION OF POLARIMETRIC SAR IMAGE BASED ON STATISTICAL REGION MERGING

Segmentation and classification of polarimetric SAR (PolSAR) imagery are very important for interpretation of PolSAR data. This paper presents a new object-oriented classification method which is based on Statistical Region Merging (SRM) segmentation algorithm and a two-level hierarchical clustering technique. The proposed method takes full advantage of the polarimetric information contained in the PolSAR data, and takes both effectiveness and efficiency into account according to the characteristic of PolSAR. A modification of over-merging to over-segmentation technique and a post processing of segmentation for SRM is proposed according to the application of classification. And a revised symmetric Wishart distance is derived from the Wishart PDF. Segmentation and classification results of AirSAR L-band PolSAR data over the Flevoland test site is shown to demonstrate the validity of the proposed method.

[1]  Gabriel Vasile,et al.  Hierarchical segmentation of Polarimetric SAR images using heterogeneous clutter models , 2009, 2009 IEEE International Geoscience and Remote Sensing Symposium.

[2]  David A. Clausi,et al.  Unsupervised Polarimetric SAR Image Segmentation and Classification Using Region Growing With Edge Penalty , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Ridha Touzi,et al.  Segmentation of textured polarimetric SAR scenes by likelihood approximation , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[4]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[5]  Carlos López-Martínez,et al.  Filtering and segmentation of polarimetric SAR images with Binary Partition Trees , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[6]  Eric Pottier,et al.  Quantitative comparison of classification capability: fully polarimetric versus dual and single-polarization SAR , 2001, IEEE Trans. Geosci. Remote. Sens..

[7]  H.T. Li,et al.  Object-oriented classification of polarimetric SAR imagery based on Statistical Region Merging and Support Vector Machine , 2008, 2008 International Workshop on Earth Observation and Remote Sensing Applications.

[8]  Lionel Bombrun,et al.  Segmentation of Polarimetric SAR Data based on the Fisher Distribution for Texture Modeling , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[9]  Robert Jenssen,et al.  Spectral Clustering of Polarimetric SAR Data With Wishart-Derived Distance Measures , 2007 .

[10]  Ieee Xplore,et al.  IEEE Transactions on Pattern Analysis and Machine Intelligence Information for Authors , 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Carlos López-Martínez,et al.  Filtering and Segmentation of Polarimetric SAR Data Based on Binary Partition Trees , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Richard Nock.  Fast and reliable color region merging inspired by decision tree pruning , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

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

[14]  Lionel Bombrun,et al.  SEGMENTATION OF POLARIMETRIC SARDATA BASED ON THE FISHER DISTRIBUTIONFOR TEXTURE MODELING , 2008 .

[15]  Frank Nielsen,et al.  Statistical region merging , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Frank Nielsen,et al.  Semi-supervised statistical region refinement for color image segmentation , 2005, Pattern Recognit..