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Directional Context Covariance Matrix for SAR Image Classification

Synthetic Aperture Radar (SAR) is one of the most important earth observing techniques these days. Texture is an important property on SAR image, which is often used in classification. However, ordinary texture descriptors in optical image processing could fail to represent the spatial information on SAR image due to the difference in imaging mechanism and image characteristics. In this letter, a local texture descriptor is proposed for SAR image – the directional context covariance matrix (DCCM). DCCM combines the pixel value and the correlation between neighboring pixels. Then, discriminative feature vectors are derived and a SAR image classification scheme is proposed based on that. Two widely used classifiers, the support vector machine and the decision tree, are adopted here. Experiments on an AIRSAR dataset demonstrate its advantages in crop classification tasks while comparing with three typical methods.