Noting the fact that the high-dimensional data composed of various polarimetric features has better decouplability than polarimetric synthetic aperture radar (PolSAR) image source data, in this letter, multiple polarimetric features are extracted and stacked to form a high-dimensional feature cube as the input of convolutional neural networks (CNNs) to improve the performance of PolSAR image classification. Directly utilizing the polarimetric features will produce a performance degradation and the recalibration of them is indispensable. However, classical feature selection methods are independent of the classifier, which means that the stimulated features may not be the classification-friendly ones. To avoid separated procedures and improve the performance, attention-based polarimetric feature selection convolutional network, called AFS-CNN, is proposed to implement end-to-end feature selection and classification. The relationship between input polarimetric features can be captured and embedded through attention-based architecture to ensure the validity of high-dimensional data classification. Experiments on two PolSAR benchmark data sets verify the performance of the proposed method. Furthermore, this work is quite flexible, which is reflected in that the proposal can be used as a plug-and-play component of any CNN-based PolSAR classifier.

Deep neural networks, especially convolutional neural networks are recently applied to synthetic aperture radar target recognition and achieved state-of-the-art results. Large amount of labeled data are needed during training period of deep neural network. However, labeling enough synthetic aperture radar data on novel classes is not feasible. In this paper, a new framework is presented by introducing triplet loss function to train a deep neural network with few labeled data. The proposed few-shot learning method is verified using Moving and Stationary Target Acquisition and Recognition data set. The results show that the proposed network has good recognition performance on limited labeled data.

In recent years, supervised learning has seen notable progress in research on aircraft target detection using synthetic aperture radar (SAR) images, but its performance is heavily restricted by the amount of labeled data. Self-supervised learning (SSL) can address this problem by pre-training on unlabeled data to extract generalizable features. However, existing SSL methods are predominantly developed based on approaches designed for natural image processing, making them unsuitable for fully understanding SAR images. Therefore, this study proposes a self-supervised contrastive learning method (SARDet-CL) that integrates feature enhancement and imaging mechanism constraints, tailored to the characteristics and requirements of aircraft target detection tasks. Specifically, SARDet-CL enhances the model’s ability to represent spatiotemporal features through spatial feature masking and multi-temporal consistency learning, and robustly extracts target features using a dynamic threshold quantization strategy. In addition, structural information is inferred based on the SAR imaging mechanism and incorporated as an auxiliary self-supervisory signal to guide the model toward a more accurate understanding of SAR images. Experimental results based on various SAR datasets show that the proposed method outperforms other pre-training techniques in down-stream SAR target detection tasks.

Land cover classification is one of the primary applications of remote sensing. Polarimetric synthetic aperture radar provides significant information of scattering mechanism for classification. The alpha angle extracted from the Cloude eigenvector decomposition has been wildly used in discriminating targets. However, the alpha angle is always overestimated in forest area, which will induce misclassification. The estimation bias of alpha angle will be discussed. An improved alpha angle estimation method is introduced to overcome the aforementioned problem. A validation process is performed by using National Aeronautics and Space Administration/Jet Propulsion Laboratory Airborne Synthetic Aperture Radar data.