In this paper, we propose a new method for unsupervised classification of terrain types and man-made objects using polarimetric S A R data. This technique is a combination of the unsupervised classification based on the polarimetric target decomposition (Cloude and Pottier, 1997) and the maximum likelihood classifier based on the complex Wishart distribution (Lee et al., 1994). The advantage of this approach is that clusters may be identified by the scattering mechanisms from the target decomposition. The effectiveness of this algorithm is demonstrated using JPUAIRSAR and SIR-C polarimetric SAR images. MTRODUCTION Terrain classification is an important polarimetric SAR (POLSAR) application.Many algorithms have been developed for supervised and unsupervised terrain classification. In principle, training sets for each class are selected manually in the supervised classification. This manual technique could be difficult to implement due to the high dimensionality of POLSAR imagery. Unsupervised classification automatically finds clusters based on a certain criterion. In most algorithms, clusters have to be interpreted manually for terrain types to be useful.Cloude and Pottier [l] proposed an unsupervised classification algorithm based on their target decomposition theory. The medium's scattering mechanisms, characterized by entropy (H) and a angle are used for classification. The H and a plane is divided into 8 zones. The physical scattering characteristic associated with each zone provides information for terrain type assignment. This distinctive advantage, unfortunately, is offset by preset zone boundaries in H and a plane. Clusters may fall on the boundary or more than one class may be enclosed in one zone. Furthermore, magnitudes are not used in the classification.Lee et a1 [2] developed a supervised algorithm based on the complex Wishart distribution for the polarimetric covariance matrix. Training sets have to be selected in advance.We propose to use the Cloude and Pottier's method to initially classify the polarimetric SAR image. The initial classification map serves as training sets for classification based on the Wishart distribution. The classified results are then used as training sets for the next iteration. Significant improvement in each iteration has been observed. The iteration ends, when the number of pixels switching classes becomes smaller than a predetermined number. We have observed that the class centers in the H -E plane are shifted after the first and subsequent iterations. The class boundaries in the H -a plane become indistinct with considerable overlap. However, the final class centers in H -E plane are useful for interpretation of terrain types. The advantage of this combined method is in its effectiveness in automated classification, and in providing interpretation based on scattering mechanism for each class. Classification Based on Target DecompositionCloude and Pottier's decomposition is based on the eigenvalue analysis of the coherency matrix, T, which is formed by the Pauli base...

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