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Synthetic aperture radar inshore ship detection based on multivariate feature fusion

Journal of Electronic Imaging 2025年第2期34卷

作者： Yan, Junhua Sang, Di Gao, Yinsen Liang, Shuo Zhang, Yin Wang, Dawei Nanjing University of Aeronautics and Astronautics College of Astronautics Nanjing China Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Key Laboratory of Space Photoelectric Detection and Perception Nanjing China Joint Laboratory of Spatial Intelligent Perception and Large Model Application Meishan China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang China

We propose an algorithm for detecting inshore ship targets in synthetic aperture radar (SAR) images, named MFF-YOLO (You Only Look Once of multivariate feature fusion). On the basis of YOLOv5, the weakly supervised saliency feature module is constructed to enhance the feature texture under the supervision of weakly supervised labels, extract the multi-scale saliency target features, solve the problem that it is difficult to locate potential target regions in the SAR image with insufficient inshore target features, and improve the recall rate by 3.2%;the land-sea feature module is constructed to extract the land-sea distribution features of the image, suppress the land area features, solve the problem of high false alarm rate caused by land clutter in inshore scenes, and improve the precision rate by 3.2%;construct multivariate feature fusion module to fuse the target features, weakly supervised saliency features and land-sea distribution features in multiple scales;and use adaptive thresholding to suppress irrelevant noise to obtain target features enhanced by multivariate features fusion, which solves the problems of insufficient target available information and irrelevant noise interference in SAR images, improves AP0.5 by 3.3%, and improves F1 measure by 2%. This algorithm achieves a precision of 88.3% on the inshore portion of the high-resolution SAR image dataset, a recall of 77.5%, an AP0.5 of 81.6%, and an F1 of 0.825, demonstrating superior detection performance over other comparative algorithms, with good accuracy and robustness. © 2025 SPIE. All rights reserved.

关键词： inshore multivariate features fusion saliency ship detection synthetic aperture radar images

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Handling Noisy Annotation for Remote Sensing Semantic Segmentation via Boundary-Aware Knowledge Distillation

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Sun, Yue Liang, Dong Li, Shao-Yuan Chen, Songcan Huang, Sheng-Jun Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing211106 China Shenzhen Research Institute Nanjing University of Aeronautics and Astronautics Shenzhen518131 China Joint Laboratory of Spatial Intelligent Perception and Large Model Application Nanjing211106 China

In recent years, image segmentation has made significant progress, but acquiring annotated data is still a considerable challenge, especially in remote sensing imagery (RSI). The complex structure and intercategory confusion of RSI increase the time consumption and cost of pixel-level annotation, and noisy annotations inevitably appear. This article proposes a boundary-aware knowledge distillation (BAKD) method to handle noisy annotations by evaluating their uncertainty. BAKD consists of two core strategies: predictive confidence evaluation (PCE) and boundary-annotated reliability evaluation (BRE). The predictive confidence (PC) jointly decided by the teacher and student networks reflects the annotation’s uncertainty. The boundary-annotated reliability (BR) directly measures the annotation’s uncertainty based on the distance from the annotation to the semantic boundary. Leveraging these two types of uncertainty information, BAKD assigns each sample a comprehensive boundary-aware weight to identify samples with potential noisy annotations. This alleviates the impact of noisy annotation on the model’s training and improves its generalization performance. Experimental results show that BAKD achieves competitive semantic segmentation performance on the Potsdam and Vaihingen benchmarks compared with the state-of-the-art KD methods. In addition, BAKD can be easily integrated into semantic segmentation methods based on KD (SSKD), extending their applicability in handling noisy annotations. © 1980-2012 IEEE.

关键词： Semantic Segmentation

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Temperature and refractive index high accuracy fiber sensing by using linewidths of FBS induced by TR 2,m

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Measurement 2025年 254卷

作者： Wujun Zhang Yuangang Lu Hongcheng Ni Yuqin Feng Chongjun He Nan Guo Key Laboratory of Space Photoelectric Detection and Perception of Ministry of Industry and Information Technology Nanjing 211106 China College of Astronautics Nanjing University of Aeronautics and Astronautics Nanjing 211106 China Joint Laboratory of Spatial Intelligent Perception and Large Model Application Meishan 620524 China

In this paper, a new method for temperature and refractive index (RI) simultaneous measurement by using the linewidths of forward Brillouin scattering (FBS) induced by torsional-radial acoustic modes ( TR 2,m ) in optical fiber is proposed. The FBS induced by TR 2,m with strong signal is detected by a new single-end reflective structure. We propose to measure temperature and RI simultaneously with high accuracy by using linewidths of two peaks of FBS corresponding to two TR 2,m . In the proof-of-concept experiment, a thin polyimide-coated G657 fiber is used as sensing fiber, in which the thin coating not only provides high sensing sensitivity, but also achieves high mechanical strength. The experimental results show that the sensor has high temperature and RI measurement accuracy of 0.17 ℃ and 0.0005, respectively. The sensor with such new detection method, new detection structure and new sensing fiber possesses the benefits of high accuracy, high mechanical strength, and simplicity, which has many potential applications in the field of biomedicine and marine monitoring.

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