Supervised learning for semantic segmentation has achieved impressive success in remote sensing, while this normally has a high demand on pixel-level ground truth from the testing images (target domain). Labeling data for semantic segmentation is labor-intensive and time-consuming. To reduce the workload of manual labeling, domain adaptation (DA) utilizes preexisting labeled images from other sources (source domain) to classify the images in the target domain. In this article, we propose a bispace alignment network for DA named BSANet. BSANet is designed to have a dual-branch structure which is able to extract features in the image domain and the wavelet domain simultaneously. To minimize the discrepancy between the source and target domains, we propose a bispace adversarial learning strategy. Specifically, BSANet employs two discriminators in different spaces, one aligning the source and target feature distributions, and the other helping the classification outputs render reasonable spatial layouts. The proposed method shows the ability to train an end-to-end network for semantic segmentation without using any label in the target domain. Extensive experiments and ablation studies are conducted in cross-city scenarios. Comparative experiments with several state-of-the-art DA methods show that our method achieves the best performance.

With the rapid development of deep learning technology, semantic segmentation methods have been widely used in remote sensing data. A pretrained semantic segmentation model usually cannot perform well when the testing images (target domain) have an obvious difference from the training data set (source domain), while a large enough labeled data set is almost impossible to be acquired for each scenario. Unsupervised domain adaptation (DA) techniques aim to transfer knowledge learned from the source domain to a totally unlabeled target domain. By reducing the domain shift, DA methods have shown the ability to improve the classification accuracy for the target domain. Hence, in this letter, we propose an unsupervised adversarial DA network that converts deep features into 2-D feature curves and reduces the discrepancy between curves from the source domain and curves from the target domain based on a conditional generative adversarial networks (cGANs) model. Our proposed DA network is able to improve the semantic labeling accuracy when we apply a pretrained semantic segmentation model to the target domain. To test the effectiveness of the proposed method, experiments are conducted on the International Society for Photogrammetry and Remote Sensing (ISPRS) 2-D Semantic Labeling data set. Results show that our proposed network is able to stably improve overall accuracy not only when the source and target domains are from the same city but with different building styles but also when the source and target domains are from different cities and acquired by different sensors. By comparing with a few state-of-the-art DA methods, we demonstrate that our proposed method achieves the best cross-domain semantic segmentation performance.

High-squint synthetic aperture radar (HS-SAR) imaging technology greatly enhances the flexibility of SAR. However, the existing HS-SAR imaging algorithms are generally based on the assumption that the illuminated target is static during the synthetic aperture formation. As a consequence, the state-of-the-art algorithms are unable to focus on moving targets well, thus leading to displaced and blurred images of the targets. A maritime ship is a typical kind of moving target with complex noncooperative motion, which has received much attention. Thus, it is a difficult but valuable issue to study HS-SAR imaging of ship targets. In this work, the influence of ship translation and fluctuation is theoretically analyzed. On the basis of these analyses, the new conception of ``third range compression (TRC)'' caused by ship translation is proposed, and the squint minimization (SM) operation is implemented for the compensation of TRC. Moreover, considering that the target Doppler parameters induced by ship fluctuation are related to its position, a novel method called WASH-CLEAN (watershed and CLEAN) is proposed to automatically focus ship scattering points with different swings. Finally, we propose an integrated modified range-Doppler (RD) imaging algorithm by combining the advantages of HS-SAR and inverse synthetic aperture radar (ISAR). The simulation results show the validity and effectiveness of the presented method.

Ground Penetrating Radar (GPR) has become indispensable for subsurface reconstruction, especially in complex, multilayered infrastructure environments. Efficient and accurate estimation of permittivity and layer thickness, crucial for understanding these scenes, presents significant challenges. This paper explores the integration and interaction between the back projection (BP) algorithm and full-wave inversion (FWI) to optimize reconstruction outcomes. We propose a ray-based refraction method and a high-order moment-oriented local BP estimation, each aimed at improving efficiency from the critical perspectives of ’delay’ and ’summation’ in the BP algorithm, respectively, with a focus on the target layer. Crucially, the BP estimation results provide a reliable initial model for full-wave inversion, reducing the risk of local minima and decreasing the iteration count for more precise parameter estimation. FWI also compensates for the BP algorithm’s limitations in estimating parameters in non-target layers. Furthermore, full-wave modeling mitigates antenna effects prior to parameter estimation, thereby enhancing accuracy. During reconstruction, the BP algorithm primarily generates target imaging, while FWI provides detailed information about layer interfaces. This synergistic approach leverages the complementary strengths of both algorithms: the BP algorithm captures spatial information depicted by the targets observed in B-scans, while FWI incorporates detailed electromagnetic wave propagation along layer interfaces presented in A-scans via radar equations. Experiments systematically analyze the accuracy and effectiveness of the proposed approach using ideal simulation models, sandbox laboratory data, and road data from the Belgian Road Research Centre facilities. This comprehensive evaluation underscores the approach’s substantial potential in advanced geophysical surveys and environmental research.

High-resolution range profile (HRRP) recognition serves as a foundational task in radar automatic target recognition (RATR), enabling robust classification under all-day and all-weather conditions. However, existing approaches often struggle to simultaneously capture the multi-scale spatial dependencies and global spectral relationships inherent in HRRP signals, limiting their effectiveness in complex scenarios. To address these limitations, we propose a novel multi-scale domain perception network tailored for HRRP-based target recognition, called MSDP-Net. MSDP-Net introduces a hybrid spatial–spectral representation learning strategy through a multiple-domain perception HRRP (DP-HRRP) encoder, which integrates multi-head convolutions to extract spatial features across diverse receptive fields, and frequency-aware filtering to enhance critical spectral components. To further enhance feature fusion, we design a hierarchical scale fusion (HSF) branch that employs stacked semantically enhanced scale fusion (SESF) blocks to progressively aggregate information from fine to coarse scales in a bottom-up manner. This architecture enables MSDP-Net to effectively model complex scattering patterns and aspect-dependent variations. Extensive experiments on both simulated and measured datasets demonstrate the superiority of MSDP-Net, achieving 80.75% accuracy on the simulated dataset and 94.42% on the measured dataset, highlighting its robustness and practical applicability.

With the capability of capturing a target’s two-dimensional information, Inverse Synthetic Aperture Radar (ISAR) imaging is widely used in Radar Automatic Target Recognition. However, changes in the ship target’s attitude can lead to the scatterers’ rotation, occlusion, and angle glint, reducing the accuracy of ISAR image recognition. To solve this problem, we proposed a Triangle Preserving level-set-assisted Triangle-Points Affine Transform Reconstruction (TP-TATR) for ISAR ship target recognition. Firstly, three geometric points as initial information were extracted from the preprocessed ISAR images based on the ship features. Combined with these points, the Triangle Preserving level-set (TP) method robustly extracted the fitting triangle of targets depending on the intrinsic structure of the ship target. Based on the extracted triangle, the TP-TATR adjusted all the ship targets from the training and test data to the same attitude, thereby alleviating the attitude sensitivity. Finally, we created templates by averaging the adjusted training data and matched the test data with the templates for recognition. Experiments based on the simulated and measured data indicate that the accuracies of the TP-TATR method are 87.70% and 90.03%, respectively, which are higher than those of the contrast algorithms and have a statistical difference. These demonstrate the effectiveness and robustness of our proposed TP-TATR method.