This paper presents a deep framework EddyDet to automatically detect oceanic eddies in Synthetic Aperture Radar (SAR) images. The EddyDet has been developed using the Mask Region with Convolutional Neural Networks (Mask RCNN) framework, incorporating two new branches: Edge Head and Mask Intersection over Union (IoU) Head. The Edge Head can learn internal texture information implicitly, and the Mask IoU Head improves the quality of predicted masks. A SAR dataset for Oceanic Eddy Detection (SOED) is specifically constructed to evaluate the effectiveness of the EddyDet model in detecting oceanic eddies. We demonstrate that the EddyDet is capable of achieving acceptable eddy detection results under the condition of limited training samples, which outperforms a Mask RCNN baseline in terms of average precision. The combined Edge Head and Mask IoU Head have the ability to describe the characteristics of eddies more correctly, while the EddyDet shows great potential in practice use accurately and time efficiently, saving manual labor to a large extent.

Traditional map style transfer methods are mostly based on GAN, which are either overly artistic at the expense of conveying information, or insufficiently aesthetic by simply changing the color scheme of the map image. These methods often struggle to balance style transfer with semantic preservation and lack consistency in their transfer effects. In recent years, diffusion models have made significant progress in the field of image processing and have shown great potential in image-style transfer tasks. Inspired by these advances, this paper presents a method for transferring real-world 3D scenes to a cartoon style without the need for additional input condition guidance. The method combines pre-trained LDM with LoRA models to achieve stable and high-quality style infusion. By integrating DDIM Inversion, ControlNet, and MultiDiffusion strategies, it achieves the cartoon style transfer of real-world 3D scenes through initial noise control, detail redrawing, and global coordination. Qualitative and quantitative analyses, as well as user studies, indicate that our method effectively injects a cartoon style while preserving the semantic content of the real-world 3D scene, maintaining a high degree of consistency in style transfer. This paper offers a new perspective for map style transfer.

We propose and experimentally demonstrate a tunable frequency-doubling optoelectronic oscillator (FD-OEO) based on a single-bandpass dispersion-induced microwave photonic filter (MPF) consisting of a Mach–Zehnder modulator (MZM), a linearly chirped fiber Bragg grating and polarization-multiplexed dual-loop. Thanks to the polarization dependence of the MZM, a special double sideband modulation is implemented where the optical carrier (OC) and subcarriers are orthogonally polarized. By simply tuning the PC in the OEO loop, the phase difference between the orthogonal polarization carrier and two sidebands can be controlled, and thus the center frequency of the fundamental OEO can be tuned. Furthermore, a PC and a polarizer are placed outside the OEO to achieve optical carrier suppression (OCS) modulation, which ensures that a frequency-tunable microwave signal at the second-harmonic frequency is generated. In the experiment, a fundamental frequency signal with tunable frequency from 3.6 to 6.85 GHz and FD-OEO with a tunable frequency range from 7.2 to 13.7 GHz are generated.

Aerial–ground visual tracking facilitates dynamic remote sensing and scene monitoring by integrating complementary observations from unmanned aerial vehicle (UAV) and ground cameras, allowing one view to compensate when the target is poorly observed or temporarily invisible in the other. However, substantial cross-view variations in target appearance, scale, and geometry make it difficult to establish reliable target correspondences and effectively fuse complementary cues. To address these challenges, we propose CVAC-Net, a Cross-View Alignment and Adaptive Collaboration Network for aerial–ground visual tracking. Specifically, CVAC-Net uses cross-view contrastive learning to reduce semantic differences and a learned spatial offset to adjust the relative positions of aerial- and ground-view target features within their normalized search crops. Next, it uses the similarity between the main-view template and the aligned auxiliary-view search features to generate an importance map that reweights redundant and target-irrelevant regions, thereby improving the reliability of feature aggregation. Finally, its view-specific branches select and weight the top-K encoder outputs to integrate complementary cross-view features. Extensive experiments on the UAV–ground visual tracking (UGVT) benchmark show that CVAC-Net improves the baseline by 3.2, 3.1, and 1.7 percentage points in success rate (SR), normalized precision rate (NPR), and precision rate (PR), respectively. We further evaluate CVAC-Net using a two-dimensional (2D) single-object tracking protocol derived from Griffin using its predefined training and validation splits, providing supplementary validation on a simulated aerial–ground distribution.