POLSAR IMAGE CLASSIFICATION BASED ON THREE-DIMENSIONAL WAVELET TEXTURE FEATURES AND MARKOV RANDOM FIELD

The speckle effect embedded in polarimetric synthetic aperture radar (PolSAR) data damages the performance of PolSAR image classification greatly. To alleviate this issue, a new supervised classification method, which introduces spatial consistency in both feature extraction and classification steps is proposed. Specifically, three-dimensional discrete wavelet transform (3D-DWT) is used to extract spectral-spatial texture features, which are proved to be more discriminative than original ones. Afterward, label smoothness prior is incorporated in the classification, which is implemented using a Markov random field (MRF). To demonstrate the validity of the proposed method, real PolSAR image is used in experiments. Compared with the other state-of-the-art methods, this method achieves higher classification accuracy and better visual spatial connectivity.