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Recently, deep learning methods have attracted much attention in the field of polarimetric synthetic aperture radar (PolSAR) data interpretation and understanding. However, for supervised methods, it requires large-scale labeled data to achieve better performance, and getting enough labeled data is a time-consuming and laborious task. Aiming to obtain a good classification result with limited labeled data, we focus on learning discriminative high-level features between multiple representations, which we call mutual information. As PolSAR data have multi-modal representations, there should have strong similarity between multi-modal features of the same pixel. In addition, each pixel has its own unique geocoding and scattering information. Hence, every pixel has great difference from other pixels in a specific representation space. Based on the above observations, this article proposes a mutual information-based self-supervised learning (MI-SSL) model to learn an implicit representation from unlabeled data. In this article, the self-supervised learning idea is first applied to PolSAR data processing. Furthermore, a reasonable pretext task, which is suitable for PolSAR data, is designed to extract mutual information for classification tasks. Compared with the state-of-the-art classification methods, experimental results on four PolSAR data sets demonstrate that our MI-SSL model produces impressive overall accuracy with fewer labeled data. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) image classification is a fundamental research direction in image interpretation. With the development of various intelligent technologies, deep learning techniques are gradually being applied to SAR image classification. In this study, a new SAR classification algorithm known as the multiscale convolutional neural network with an autoencoder regularization joint contextual attention network (MCAR-CAN) is proposed. The MCAR-CAN has two branches: the autoencoder regularization branch and the context attention branch. First, autoencoder regularization is used for the reconstruction of the input to regularize the classification in the autoencoder regularization branch. Multiscale input and an asymmetric structure of the autoencoder branch cause the network more to be focused on classification than on reconstruction. Second, the attention mechanism is used to produce an attention map in which each attention weight corresponds to a context correlation in attention branch. The robust features are obtained by the attention mechanism. Finally, the features obtained by the two branches are spliced for classification. In addition, a new training strategy and a postprocessing method are designed to further improve the classification accuracy. Experiments performed on the data from three SAR images demonstrated the effectiveness and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In order to better interpret polarimetric synthetic aperture radar (PolSAR) images, many scholars tend to do target decomposition for PolSAR images and utilize the obtained features to perform subsequent classification. These target decomposition features play an important role in terrain classification but completely utilizing them produces a high computational complexity. Furthermore, some features have a negative impact on the classification task. Therefore, selecting the appropriate amount of high-quality features is of great significance to the classification task. In this paper, we propose a convolutional neural network (CNN)-based feature selection algorithm for PolSAR image classification. First, we design a 1-D CNN for feature selection, then train the designed network with all the decomposition features to obtain a trained model. Second, the Kullback–Leibler distance (KLD) between different features is utilized as a standard to select feature subsets. Third, feature subsets with excellent performance form the final results. Due to the special structure of the 1-D CNN, repetitively training model is avoided when the input changes. Different from traditional feature selection methods, our method considers the performance of features combination rather than single feature contribution. To this end, the feature subsets selected by the proposed method are more useful to the classification task. Innovatively introducing KLD in the selection stage avoids random selection and improves the selection efficiency. Finally, we validate the performance of selected feature subsets in traditional and deep learning classification frameworks. Experiments demonstrate that features selected by the proposed method have a good performance comparing with others on three real PolSAR data sets. [ABSTRACT FROM AUTHOR]