English(EN) Semi-Supervised Hyperspectral Image Classification with Edge-Aware Superpixel Label Propagation and Adaptive Pseudo-Labeling

新框架通过新颖的标签传播改进高光谱图像分类

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-07 04:00

研究人员开发了一种新颖的半监督高光谱图像分类框架，解决了边界标签扩散和伪标签不稳定性等挑战。所提出的系统集成了边缘感知超像素标签传播（EASLP）模块，以减轻标签扩散并增强边界区域的鲁棒性。此外，动态历史融合预测（DHP）方法和自适应三方样本分类（ATSC）策略协同工作，通过分层利用不同样本类型来稳定伪标签并提高学习效率。 AI

影响这项研究可能在遥感和医学成像等领域带来更准确、更高效的高光谱图像分析。

排序理由该条目是一篇学术论文，详细介绍了一种用于高光谱图像分类的新颖方法。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Yunfei Qiu, Qiqiong Ma, Tianhua Lv, Li Fang, Shudong Zhou, Wei Yao · 2026-09-07 04:00

基于边缘感知超像素标签传播和自适应伪标签的半监督高光谱图像分类

arXiv:2601.18049v2 Announce Type: replace Abstract: Significant progress has been made in semi-supervised hyperspectral image (HSI) classification regarding feature extraction and classification performance. However, due to high annotation costs and limited sample availability, s…

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基于边缘感知超像素标签传播和自适应伪标签的半监督高光谱图像分类

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