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(Objective)Synthetic aperture radar (SAR) is a kind of high-resolution imaging sensor, which is able to work under nearly all weather and illumination conditions. SAR plays an important role in earth observation. For single-band, single-polarization SAR image, however, there’s only one complex scalar in each pixel. So that the information contained in such single-channel SAR image could be quite limited, which limits its performance in various applications. Since terrain classification is one of the typical tasks in SAR image interpretation, this paper takes it as an example to demonstrate the problem and gives our solution. (Method)To address the above problem, this paper proposes a representation for the spatial information on SAR image—the directional context covariance matrix (DCCM). DCCM obtains the variance of pixel intensity in several orientations inside the neighborhood in order to make use of the context information. During such process, the target pixel in extended from a complex scalar to a group of matrices, so that its information content is increased. Besides, the matrix form also enables some of the advanced matrix algorithms to be applied to single-channel SAR image. On the basis of it, the DCCM texture feature is derived, which can better represent the texture properties on SAR image and shows better discriminability for different land covers. Then, the texture feature is combined with two traditional classifiers as well as the convolutional neural network (CNN), respectively. Thereafter, a SAR image classification scheme is established. (Result)To illustrate the performance of proposed method, terrain classification experiments are carried out on AIRSAR and UAVSAR datasets. Methods based on three commonly used texture features, the gray level co-occurrence matrix (GLCM), Gabor filters and multilevel local pattern histogram (MLPH) are taken into comparison. On traditional classifiers, the overall classification accuracies are increased by 7% on both datasets. While combining with CNN, the overall accuracies and kappa coefficients are significantly improved with DCCM texture feature than the original SAR data. The proposed feature also shows nice efficiency and better robustness when compared to other texture features. (Conclusion)The experiment results indicate that DCCM is an effective representation that is suitable for SAR image. DCCM is efficient, robust and easy-to-use. The proposed DCCM based classification method can improve the classification performance of single-channel SAR image by increasing the pixel information content. Beyond that, DCCM could be a promising method for many other SAR image interpretation tasks.