SAR Aircraft Segmentation With SAR-to-Optical Image Translation and Segment Anything Model

IF 5.3 2区 地球科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Ruixi You;Feng Xu;Min Liu
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引用次数: 0

Abstract

Instance segmentation in synthetic aperture radar (SAR) imagery has demonstrated notable success for certain target types such as vehicles and ships. However, accurate segmentation of aircraft in SAR images remains a significant challenge due to complex structural geometries, low-intensity and sparse backscattering, and frequent occurrences of incomplete or ambiguous contours. These inherent limitations hinder the generation of high-quality annotations and restrict downstream applications to coarse object detection tasks. To address these issues, this work proposes a two-stage framework combining SAR-to-optical image translation with an adapter-tuned segment anything model (SAM). In the first stage, a diffusion-based generative model first translates SAR aircraft slices into high-fidelity optical counterparts, enhancing structural visibility with continuous and interpretable contours. In the second stage, the SAM-adapter-based module produces instance-level and component-level masks on the translated images. In addition, to further improve alignment with SAR-specific characteristics, a scattering-aware refinement module refines the masks using the physical scattering distributions of the original SAR images. Experimental results demonstrate the effectiveness of the proposed framework in fine-grained segmentation and validate its strong zero-shot generalization ability, indicating its potential for scalable, automated mask annotation of aircraft in SAR imagery.
基于SAR-to- optical图像平移和任意分割模型的SAR飞机分割
合成孔径雷达(SAR)图像的实例分割在特定目标类型(如车辆和船舶)中取得了显著的成功。然而,由于复杂的结构几何形状,低强度和稀疏的后向散射,以及经常出现的不完整或模糊的轮廓,在SAR图像中准确分割飞机仍然是一个重大挑战。这些固有的限制阻碍了高质量注释的生成,并将下游应用程序限制在粗糙的对象检测任务上。为了解决这些问题,本工作提出了一个两阶段框架,将sar到光学图像的转换与适配器调谐的分段任意模型(SAM)相结合。在第一阶段,基于扩散的生成模型首先将SAR飞机切片转换为高保真光学对应,通过连续和可解释的轮廓增强结构可视性。在第二阶段,基于sam适配器的模块在翻译后的映像上生成实例级和组件级掩码。此外,为了进一步改善与SAR特定特性的对齐,散射感知细化模块利用原始SAR图像的物理散射分布对掩模进行细化。实验结果证明了该框架在细粒度分割方面的有效性,并验证了其强大的零点泛化能力,表明了其在SAR图像中可扩展的、自动的飞机掩模标注方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
9.30
自引率
10.90%
发文量
563
审稿时长
4.7 months
期刊介绍: The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.
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