Role of polarimetric indices based on statistical measures to identify various land cover classes in ALOS PALSAR data

The present paper deals with the potential application of fully polarimetric data in identification of various land cover types based on polarimetric indices, namely, backscattering coefficients and their ratios of various polarizations (linear, circular, linear 45°), entropy, weighted polarimetric sum, correlation coefficient, normalized difference polarization index, and ratio vegetation index. In order to make decision boundaries for separating land cover classes two statistical measures, namely, median and standard deviation were critically analyzed for each polarimetric parameter. In order to select the appropriate value of all polarimetric indices for each class, a relationship was obtained between that particular polarimetric index, and their median and standard deviation along with some integer, the value of which was chosen to optimize the proposed method. The proposed method was successfully applied on ALOS PALSAR data for which satisfactory results were obtained.

[1]  Seiho Uratsuka,et al.  A Study on Polarimetric Correlation Coefficient for Feature Extraction of Polarimetric SAR Data , 2005, IEICE Trans. Commun..

[2]  Yunjin Kim,et al.  Comparison of forest parameter estimation techniques using SAR data , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[3]  Thuy Le Toan,et al.  Agriculture classification using POLSAR data , 2005 .

[4]  Yoshio Yamaguchi,et al.  Land Cover Classification of Palsar Images by Knowledge Based Decision Tree Classifier and Supervised Classifiers Based on SAR Observables , 2011 .

[5]  Ankush Mittal,et al.  An Efficient Contextual Algorithm to Detect Subsurface Fires With NOAA/AVHRR Data , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Thuy Le Toan,et al.  Forest Biophysical Parameter Estimation Using L- and P-Band Polarimetric SAR Data , 2009, IEEE Transactions on Geoscience and Remote Sensing.

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

[8]  Ankush Mittal,et al.  A RoughSetClassifilcation BasedApproach to Detect Hotspots inNOAA/AVHRR Images , 2006 .

[9]  A. Walker,et al.  Classification of urban SAR imagery using object oriented techniques , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[10]  Jakob J. van Zyl,et al.  A General Characterization for Polarimetric Scattering From Vegetation Canopies , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Matthieu Molinier,et al.  Polarimetric SAR Data in Land Cover Mapping in Boreal Zone , 2010, IEEE Transactions on Geoscience and Remote Sensing.

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

[13]  CAO Yun-gang,et al.  EXTRACTION OF INFORMATION ON GEOLOGY HAZARD FROM MULTI-POLARIZATION SAR IMAGES , 2008 .

[14]  Simonetta Paloscia,et al.  The potential of multifrequency polarimetric SAR in assessing agricultural and arboreous biomass , 1997, IEEE Trans. Geosci. Remote. Sens..

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