Proceedings of International Conference on Microwave - 08 Classification of Polarimetric Synthetic Aperture Radar Images from SIR-C and ALOS PALSAR Varsha Turkar and Y.S. Rao Centre of Studies in Resources Engineering, lIT, Bombay, Powai, Mumbai-400 076, India Abstract - SIR-C quad-pol MLC data and ALOS PALSAR quad-pol and dual pol SLC data over Indian sites have been processed using PolSARpro software for classification of various land features. The land features include ocean, clear water, settlements, agriculture fields, arid lands, grown and young forest, hilly terrain, mangrove forest, etc. The test sites used are SIR-C L-band and C-band Kolkata city and its surroundings, ALOS PALSAR data over West Bengal, Haryana, Rajasthan, Uttar Pradesh, and Mumbai. For Kolkata city we observed that classification results for L- and C-bands are slightly different. For Mumbai dual pol data classification accuracy is poor due to overlap of backscattering values for bare ground and mangrove vegetation with ocean backscattering values. Index Terms - Radar polarimetry, synthetic aperture radar, speckle, target decomposition, terrain classification. I. INTRODUCTION Classification of polarimetric SAR images has become a very important topic after the availability Polarimetric SAR images through ENVISAT ASAR, ALOS PALSAR and Radarsat-2. Classification is the task of assigning a set of given data elements to a given set of labels or classes such that the cost of assigning the data element to a class is minimum. Remote-sensing research focusing on image classification is important classification results are the basis for many envIronmental and socioeconomic applications. Classifying remotely sensed data into a thematic map is very challenging because of many factors such as the complexity of the landscape in a study area, selected remotely sensed data, image-processing and classification approaches may affect the success of a classification. The major steps of image classification may include determination of a suitable classification system, selection of training samples, image preprocessing and feature extraction, selection of suitable classification approaches, post-classification processing and accuracy assessment. The two main techniques for image classification are supervised and unsupervised classification Unsupervised classification technique, claSSIfies the image automatically by finding the clusters based on certain criterion. On the other hand in supervised classification technique the location and the identity of some cover type, for example urban, forest, and water are known before. The data is collected by a field work, maps, and personal experience. The analyst tries to locate these areas on the remotely sensed data. These areas are known as "training sites". An analyst can guide a classifier with the help of these training sites 978-1-4244-2690-4444/08/$25.00©2008 IEEE 438 to learn the relationship between the data and the classes. manual technique of selecting training sets could be dIfficult when ground truth is not available. this paper, both supervised and unsupervised technIques are used for classification. The steps followed for the classifications are: 1. Calculation of Coherency or Covariance matrix. 2. Speckle removal with different Speckle filters. 3. Target Decomposition (using H, A and a) 4. Supervised and Unsupervised Image Classification Techniques. The data acquired is processed by PolSARpro ver3.0 software for classification of various land features. The software is freely available on the internet developed by ESA. II. COHERENCY AND COVARIANCE MATRICES The reflectivity of the area being observed at a given radar wavelength can be represented by "scattering matrix". Each of the four complex elements of this matrix is the amplitude and phase of the backscattered radiation as measured at one of four orthogonal transmit/receive polarizations: HH, HV, VH, andVV. There are different target polarimetric descriptors: Sinclair, Vectors, Kennaugh, Muller, Coherency and Covanance. We have used Coherency matrix for and Covariance matrix for SIR-C data. By using dIfferent basis (Lexiogrphic or Pauli) we can get two types of target vectors. The scattering vector or covariance vector is a vectorized version of the scattering matrix. It is easy to construct a power domain representation of the scattering properties, which is done by forming the product of this vector itself. The result is a covar!ance matrix which fully describes the scattering propertIes of the target. The coherency matrix is similar to the covariance matrix. To obtain the coherency matrix the matrix is vectorized in different way using the Pault spIn elements. The eigenvalues of covariance and the coherency matrix are real and are the same. The sum of the diagonal elements (trace) of both matrices is also the same and represents the total power of the The .first diagonal element of coherency matnx gIves the Information about single bounce scattering, the second diagonal element gives the information about double bounce scattering and the third diagonal element gives the information about volume scattering. In order to get Pauli RGB image which is very much used, these diagonal elements are given blue, red and green colour respectively. Both covariance and