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English(EN) Exploring Efficient Open-Vocabulary Segmentation in the Remote Sensing

新模型通过先进的特征适应和边界细化增强遥感图像分割

作者 PulseAugur 编辑部 · [3 个来源] ·

两篇新的研究论文提出了用于遥感图像语义分割的先进方法。第一篇FE-SAM在Segment Anything Model (SAM)的基础上，引入了频率调制适配器（Frequency-Modulated Adapter）以更好地适应不同地物类型的特征，并引入了EGRefiner来增强边界细节。第二篇BASeg利用马氏距离-角度边界损失（Mahalanobis-Angle Boundary Loss, MABL）来提高边界和形状的一致性，并集成了全局视觉状态空间模块（Global Visual State Space module）和跨特征融合模块（Cross-Feature Fusion module）以获取上下文和局部细节。两种方法在基准数据集上都表现出优越的性能，其中BASeg的mIoU提高了高达2.8%。 AI

影响 语义分割的这些进步可以通过更准确的图像解读来改善土地覆盖分析、城市规划和环境监测。

排序理由 两篇在arXiv上发表的学术论文，提出了遥感语义分割的新方法。

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

新模型通过先进的特征适应和边界细化增强遥感图像分割

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两篇在arXiv上发表的学术论文，提出了遥感语义分割的新方法。
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报道来源 [3]

  1. arXiv cs.AI TIER_1 English(EN) · Bingyu Li, Haocheng Dong, Da Zhang, Zhiyuan Zhao, Junyu Gao, Xuelong Li ·

    探索遥感影像的高效开放词汇分割

    arXiv:2509.12040v3 Announce Type: replace-cross Abstract: Open-Vocabulary Remote Sensing Image Segmentation (OVRSIS), an emerging task that adapts Open-Vocabulary Segmentation (OVS) to the remote sensing (RS) domain, remains underexplored due to the absence of a unified evaluatio…

  2. arXiv cs.CV TIER_1 English(EN) · Feng Gao, Zizhe Pan, Haoting Wang, Ruzhuang Hua, Jingchao Cao, Junyu Dong, Qian Du ·

    面向遥感图像语义分割的频率与边缘引导分割一切模型

    arXiv:2608.15054v1 Announce Type: new Abstract: Remote sensing image semantic segmentation (RSISS) has attracted significant attention due to the growing demand for fine-grained land cover information. The Segment Anything Model (SAM), proposed as a foundation vision model, offer…

  3. arXiv cs.CV TIER_1 English(EN) · Yuexi Song, Kailai Sun, Zhuoyu Wang, Mingyi He, Paul Pu Liang, Shenhao Wang, Jinhua Zhao ·

    BASeg：具有结构惩罚的边界感知遥感分割

    arXiv:2608.15683v1 Announce Type: new Abstract: Semantic segmentation is a core computer vision task in the remote sensing field, accelerating advancements in ur- ban development, agriculture, ecology, water resources, and environmental monitoring. However, recent methods usually…

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