[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4090":3,"related-4090":67},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":21,"classification":22,"search_phrases":45,"slug":46,"view_count":47,"doi":48,"paper":49,"created_at":66},4090,"Single-Cavity Dual-Comb Mueller Polarimetry for Material-Aware LiDAR","https:\u002F\u002Fdoi.org\u002F10.1021\u002Facsomega.6c08407","Abstract We demonstrate a single-cavity dual-comb Mueller-matrix polarimetric LiDAR that rapidly retrieves full 4 × 4 Mueller matrices without external polarization-state generators or polarization-state analyzers. The key advance is that the intrinsic polarization beating of the polarization-multiplexed dual-combs from the single source provides a self-scanning incident Stokes-state basis, replacing the sequential polarization-state generation normally required in active Mueller polarimetry. The recovered Mueller matrices are decomposed into physically interpretable descriptors, including diattenuation, polarizance, retardance, depolarization, and polarimetric-purity indices, to construct a multidimensional material-fingerprint space. Experiments on aluminum, wood, plastic, and mirror targets show clear separation of material classes in this physics-based feature space. Combining convex-hull analysis with machine-learning classification yields 93.75% accuracy. These results establish dual-comb Mueller-matrix polarimetry as a compact and practical framework for material-aware remote sensing and other applications requiring interpretable polarization contrast.","我们展示了一种单腔双梳穆勒矩阵偏振激光雷达，无需外部偏振态发生器或偏振态分析器即可快速获取完整的4×4穆勒矩阵。其关键进展在于，来自单一光源的偏振复用双梳的固有偏振拍频提供了自扫描入射斯托克斯态基，取代了主动穆勒偏振测量中通常所需的顺序偏振态生成。所恢复的穆勒矩阵被分解为物理上可解释的描述符，包括二向衰减、偏振度、延迟、退偏振和偏振纯度指数，以构建多维材料指纹空间。对铝、木材、塑料和镜面目标的实验表明，在该基于物理的特征空间中材料类别清晰分离。将凸包分析与机器学习分类相结合，可获得93.75%的准确率。这些结果确立了双梳穆勒矩阵偏振测量作为一种紧凑且实用的框架，适用于材料感知遥感及其他需要可解释偏振对比度的应用。",null,"ACS Omega","2026-10-01T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文为双梳偏振激光雷达的材料感知研究，属通用光学测量与遥感技术，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[19],{"name":10,"url":6},[12],[],{"status":23,"locked":14,"version":24,"method":25,"evidence":26},"needs_review","2026-10-22.2","rules",{"candidates":27,"agricultural_evidence":14,"reason":44},[28,37],{"code":29,"score":30,"supported":31,"title":32,"abstract":33,"tags":36},"I3",6,true,[],[34,35],"遥感","remote sensing",[],{"code":38,"score":39,"supported":31,"title":40,"abstract":41,"tags":43},"I2",3,[],[42],"机器学习",[],"农业相关性需要复核",[],"Single-CavityDual-CombMuellerPolarimetry-4090",1,"10.1021\u002Facsomega.6c08407",{"doi":48,"openalex_id":50,"authors":51,"venue":10,"cited_by_count":15,"oa_url":58,"card":59,"direction":63,"ingested_from":65},"W7215082915",[52,55],{"name":53,"orcid":54},"Hani J. Kbashi","https:\u002F\u002Forcid.org\u002F0000-0002-6343-248X",{"name":56,"orcid":57},"Sergey V. Sergeyev","https:\u002F\u002Forcid.org\u002F0000-0002-1515-5019","https:\u002F\u002Fpubs.acs.org\u002Facsodf\u002Farticle-pdf\u002Fdoi\u002F10.1021\u002Facsomega.6c08407\u002F68724378\u002Facsomega.6c08407.pdf",{"tldr":60,"method":61,"finding":62,"direction":63,"opportunity":64},"提出单腔双梳穆勒矩阵偏振激光雷达，无需外部偏振器件即可快速获取全穆勒矩阵并识别材料。","单腔偏振复用双梳的本征偏振拍频实现自扫描入射斯托克斯基，结合凸包与机器学习分类。","铝、木、塑料和镜面目标在物理特征空间清晰分离，分类准确率达93.75%。","农业遥感与作物表型","将该紧凑偏振激光雷达用于作物冠层与果实材料感知，探索偏振指纹与农学参数的关联。","openalex","2026-10-03T23:30:34.082544Z",{"total":15,"page":47,"page_size":47,"items":68},[]]