Accurate baseline of synthetic aperture radar (SAR) interferometry (InSAR) is an important parameter for geodetic application of the InSAR data. Although some advanced SAR satellites have precise orbit determination, there are still many SAR satellites suffering from baseline inaccuracies, such as GF-3. In this paper, an image-based estimator for baseline correction is proposed, which requires only the external digital elevation model (DEM) data. The idea of the method is to project the orbit error phase onto the phase components carrying the baseline error information, which is called orbit error phase bases in this paper, and to correct the baseline according to the projection coefficients. Since the pure orbit error phase is unavailable, the residual phase of the interferogram is used to approximate the orbit error phase, and a series of processes are introduced to weaken the effect of this approximation. Both the simulated and real data from GF-3 SAR are used to validate the proposed method, and a comparison with the conventional nonlinear least-square and the latest proposed flat-earth phase based baseline refinement methods are made. The results indicated superior accuracy and robustness of our method especially in areas with higher relief and wider coverage.

Deep neural networks (DNNs) have achieved great success in various computer vision tasks. However, they are susceptible to artificially designed adversarial perturbations, which limit their deployment in security-critical applications. In this paper, we propose a gradual adversarial training (GAT) method for remote sensing image segmentation. Our method incorporates a domain-adaptive mechanism that dynamically modulates input data, effectively reducing adversarial perturbations. GAT not only improves segmentation accuracy on clean images but also significantly enhances robustness against adversarial attacks, all without necessitating changes to the network architecture. The experimental results demonstrate that GAT consistently outperforms conventional standard adversarial training (SAT), showing increased resilience to adversarial attacks of varying intensities on both optical and Synthetic Aperture Radar (SAR) images. Compared to the SAT defense method, GAT achieves a notable defense performance improvement of 1% to 12%.

Active jamming in complex electromagnetic environments can severely degrade radar remote sensing imaging and target detection, especially when deceptive and suppressive jamming components coexist. Existing deep learning methods usually formulate jamming recognition as a closed set classification task, which provides limited information about component superposition, time-frequency localization, and physical jamming parameters. To address these limitations, this paper proposes a prior-guided lightweight dual-task network for structured composite active jamming cognition. The proposed framework extracts multi-domain handcrafted features and decision tree based coarse priors from the received signal, and fuses them with short-time Fourier transform (STFT) time-frequency images through a confidence-gated MobileViT_CA-based network. The recognition branch predicts the jamming family, fine-grained class, and composite attributes, while the segmentation branch estimates component masks for copy, convolution, and noise components. A FiLM-conditioned mask refinement module further improves mask continuity and boundary quality, enabling the extraction of physical parameters such as bandwidth, center frequency, coverage duration, delay, slice width, and repetition interval. Experiments on a 22-class active jamming dataset show that the proposed method, built on a 1.92 M-parameter MobileViT_CA backbone, achieves 93.78% overall classification accuracy, 96.49% jamming family accuracy, and 89.17% accuracy on the 12 composite classes. The FiLM-conditioned mask refinement module improves the test-set mIoU from 67.55% to 82.93%, and most representative physical parameters are estimated with relative errors below 15%.