With the development of remote sensing technology, content-based remote sensing image retrieval has become a research hotspot. Remote sensing image datasets not only contain rich location, semantic and scale information but also have large intra-class differences. Therefore, the key to improving the performance of remote sensing image retrieval is to make full use of the limited sample information to extract more comprehensive class features. In this paper, we propose a proxy-based deep metric learning method and an adaptive multi-proxy framework. First, we propose an intra-cluster sample synthesis strategy with a random factor, which uses the limited samples in batch to synthesize more samples to enhance the network’s learning of unobvious features in the class. Second, we propose an adaptive proxy assignment method to assign multiple proxies according to the cluster of samples within a class, and to determine weights for each proxy according to the cluster scale to accurately and comprehensively measure the sample-class similarity. Finally, we incorporate a rigorous evaluation metric mAP@R and a variety of dataset partitioning methods, and conduct extensive experiments on commonly used remote sensing image datasets.

Accurate detection of sea ice leads is essential for safe navigation in polar regions. In this paper, a shape-aware (SA) network, SA-DeepLabv3+, is proposed for automatic lead detection from synthetic aperture radar (SAR) images. Considering the fact that training data are limited in the task of lead detection, we construct a dataset fusing dual-polarized (HH, HV) SAR images from the C-band Sentinel-1 satellite. Taking the DeepLabv3+ as the baseline network, we introduce a shape-aware module (SAM) to combine multi-scale semantic features and shape information and, therefore, better capture the shape characteristics of leads. A squeeze-and-excitation channel-position attention module (SECPAM) is designed to enhance lead feature extraction. Segmentation loss generated by the segmentation network and shape loss generated by the shape-aware stream are combined to optimize the network during training. Postprocessing is performed to filter out segmentation errors based on the aspect ratio of leads. Experimental results show that the proposed method outperforms the existing benchmarking deep learning methods, reaching 96.82% for overall accuracy, 93.01% for F1-score, and 91.48% for mIoU. It is also found that the fusion of dual-polarimetric SAR channels as the input could effectively improve the accuracy of sea ice lead detection.

Sea ice segmentation based on Synthetic Aperture Radar (SAR) images has become an important technical means for polar climate change monitoring and navigation safety guarantee. However, the existing methods have limitations in the utilization of SAR polarization information and the modeling of local diversity details of sea ice, which leads to insufficient segmentation, especially in complex ice-water boundary regions. To address these issues, this paper proposes a novel Polarization-Fused Edge-Enhanced UNet (PFEE-UNet) designed specifically for sea ice segmentation from high-resolution SAR images. Specifically, we design the Cross-Polarization Channel Interaction (CPCI) module, which employs a dual interaction strategy of hierarchical inter-group cascading and symmetric cross-fusion. This approach effectively leverages the complementary features of the HH and HV polarization channels, significantly enhancing the distinction between sea ice and open water. Additionally, we present the Dense–Sparse Diversity Enhancement (DSDE) module, which combines a spatial-channel joint attention mechanism to strengthen the model’s ability to capture spatial relationships within complex ice–water structures, effectively alleviating misclassifications caused by abrupt local texture changes. Finally, we design the Selective Edge Fusion (SEF) module, which dynamically selects and integrates multi-level edge features, improving the continuity of sea ice boundaries and preserving its morphological integrity. The experimental results show that the proposed PFEE-UNet model outperforms mainstream segmentation methods on the AI4Arctic/ASIP sea ice dataset, achieving an average Intersection over Union (IoU) of 84.48%, which surpasses existing methods such as HRNet (82.52%) and DeepLabv3+ (82.40%). Additionally, PFEE-UNet was applied for end-to-end ice–water segmentation on real-world Sentinel-1 SAR scenes, demonstrating its effectiveness and robustness for practical sea ice monitoring.

Maritime target detection under complex adverse weather conditions (e.g., fog, rain, and low light) is crucial for Unmanned Surface Vehicle (USV) navigation. However, achieving high detection accuracy and efficiency remains challenging due to coupled environmental interference and limited computing resources. In this paper, we propose W-MTD, a task-specific distillation framework designed for weather-robust and lightweight maritime target detection based on knowledge distillation. Building upon the Fine-grained Distribution Refinement (D-FINE) detection model, this method constructs a dual-path knowledge distillation framework tailored for maritime scenes. Through the synergistic optimization of feature similarity constraints and decoupled distillation, it facilitates multi-level knowledge transfer from a teacher model to a lightweight student model, mitigating feature degradation caused by model compression. A multi-scenario augmentation strategy is designed to balance convergence across different weather conditions. Experiments show that W-MTD’s student model improves detection accuracy by 7.0–13.9% under three adverse weather conditionscompared to the baseline teacher model trained solely on clear weather data while maintaining comparable clear-weather performance. With only 4 M parameters and 7 GFLOPs, the student model demonstrates favorable performance and efficiency compared to other real-time detectors, indicating its potential suitability for USV deployment.

Virtual Try-Off (VTOFF) is a challenging multimodal image generation task that aims to synthesize high-fidelity flat-lay garments under complex geometric deformation and rich high-frequency textures. Existing methods often rely on lightweight modules for fast feature extraction, which struggles to preserve structured patterns and fine-grained details, leading to texture attenuation during generation.To address these issues, we propose AlignVTOFF, a novel parallel U-Net framework built upon a Reference U-Net and Texture-Spatial Feature Alignment (TSFA). The Reference U-Net performs multi-scale feature extraction and enhances geometric fidelity, enabling robust modeling of deformation while retaining complex structured patterns. TSFA then injects the reference garment features into a frozen denoising U-Net via a hybrid attention design, consisting of a trainable cross-attention module and a frozen self-attention module. This design explicitly aligns texture and spatial cues and alleviates the loss of high-frequency information during the denoising process.Extensive experiments across multiple settings demonstrate that AlignVTOFF consistently outperforms state-of-the-art methods, producing flat-lay garment results with improved structural realism and high-frequency detail fidelity.