Multiscale SAR Image SegmentationusingWavelet - domain Hidden Markov Tree

We study the segmentation of SAR imagery using wavelet-domain Hidden Markov Tree (HMT) models. The HMT model is a tree-structured probabilistic graph that captures the statistical properties of the wavelet transforms of images. This technique has been successfully applied to the segmentation of natural texture images, documents, etc. However, SAR image segmentation poses a diicult challenge owing to the high levels of speckle noise present at ne scales. We solve this problem using a \truncated" wavelet HMT model specially adapted to SAR images. This variation is built using only the coarse scale wavelet coeecients. When applied to SAR images, this technique provides a reliable initial segmentation. We then reene the classiication using a multiscale fusion technique, which combines the classiication information across scales from the initial segmentation to correct for misclassiications. We provide a fast algorithm, and demonstrate its performance on MSTAR clutter data.