For synthetic aperture radar (SAR) image classification problem, we need to take into account unlabeled datasets containing unknown classes crossing different satellites. In this letter, a spherical space domain adaptation (DA) network under open set condition is proposed to solve this problem. First, we transform the prior Euclidean feature space into the spherical space to construct a classification network such that features of the same class of SAR images are clustered together and features of different or unknown classes are separated on the hypersphere. Second, a correction module is designed to increase the accuracy of the pseudo-label obtained by the classifier. Then, based on the adversarial learning strategy, we introduce the gradient alignment module to achieve better alignment of the source and target domains. Finally, tests on two SAR benchmark datasets from distinct satellites show that the proposed network outperforms state-of-the-art (SOTA) approaches in terms of classification accuracy.

Traditional deep learning Synthetic Aperture Radar (SAR) image object detection methods fail to provide effective detection results when faced with SAR image datasets with different joint probability distributions obtained from multiple imaging satellites. In this article, an automatic SAR image object detection method based on domain adaptation is proposed to adapt to unlabeled target domain datasets acquired by different satellites. On the basis of introducing an adversarial domain adaptation learning strategy, we propose Adversarial Learning Attention (ALA) and Compensation Loss Module (CLM) on the baseline network. In ALA, considering the great difference in the scattering intensity of SAR images, the entropy vector can be used to distinguish the high-entropy and low-entropy regions among them and assign the corresponding weights, based on which the adversarial domain adaptation learning attention is proposed to achieve instance-level feature alignment and pixel-level feature alignment in source domain and target domain, respectively. In CLM, the domain alignment of pixel-level feature and instance-level feature of SAR image objects is first implemented, and then to make the feature alignment of both domains more accurate, better aggregation of proposals of different classes of prototype objects in the same domain is required, and further compensation loss is proposed to further restrict the prototype alignment of SAR object features in both domains. We conduct experiments on two SAR datasets obtained from different satellites whose results show the superiority of the proposed method over the state-of-the-art (SOTA) methods and the effectiveness of the proposed module in improving the detection accuracy.

In the face of synthetic aperture radar (SAR) image object detection with different distributions of training and test data, traditional supervised learning methods cannot achieve good detection performance. Domain adaptation (DA) method has been shown to have the ability to solve this problem, but existing DA object detection algorithms all use adversarial DA theory for the detection task, which is ineffective in solving object regression localization in the detection task. In this article, to better solve the above problem, an automatic SAR image ship detection method based on feature decomposition crossing different satellites is proposed. The feature extraction layer of backbone network is divided into low level and high level, where domain-invariant feature (DIF) extractors are designed for the local features extracted from the low level and the global features extracted from the high level, respectively. We argue that the local and global features extracted from source domain and target domain contain domain-specific features (DSF) for adversarial DA and DIFs that contribute to object regression localization. Then, we decompose the local features and global features into DSF and DIF via vector decomposition method. For DSF counterpart, we introduce adversarial DA attention for feature alignment. DIF from the local features are fused into the backbone network for high-level global feature extraction. Finally, using region proposal network and adversarial domain classifier, we can get the accurate bounding box and object class of SAR image objects. Extensive experiments prove that the proposed method outperforms the state-of-the-art methods in terms of detection performance.

Synthetic aperture radar (SAR) imaging has developed rapidly in recent years. Although the traditional sparse optimization imaging algorithm has achieved effective results, its shortcomings are slow imaging speed, large number of parameters, and high computational complexity. To solve the above problems, an end-to-end SAR deep learning imaging algorithm is proposed. Based on the existing SAR sparse imaging algorithm, the SAR imaging model is first rewritten to the SAR complex signal form based on the real-value model. Second, instead of arranging the two-dimensional echo data into a vector to continuously construct an observation matrix, the algorithm only derives the neural network imaging model based on the iteration soft threshold algorithm (ISTA) sparse algorithm in the two-dimensional data domain, and then reconstructs the observation scene through the superposition and expansion of the multi-layer network. Finally, through the experiment of simulation data and measured data of the three targets, it is verified that our algorithm is superior to the traditional sparse algorithm in terms of imaging quality, imaging time, and the number of parameters.