{
  "abstract": "Effective monitoring of desert rangeland ecosystems is of crucial significance to regional ecological security. Unmanned aerial vehicle (UAV) hyperspectral remote sensing provides an effective means for the fine identification of vegetation species. However, in practical applications, vegetation classification using hyperspectral images often faces the problem of limited labeled samples, which makes it difficult for traditional deep learning methods to obtain stable and accurate classification results. To address these issues, this study proposes a meta-learning with Mamba (MetaMamba) method for vegetation classification in desert rangeland. This method constructs a local-global dual-branch structure in the feature extraction stage to achieve effective fusion of local and global spatial context information. Specifically, the local branch uses convolutional neural networks (CNNs) to extract fine-grained spatial features, while the global branch models long-distance spatial dependencies based on the Mamba model. Additionally, a meta-learning strategy is introduced to enhance the feature learning and generalization abilities of the model under few-shot conditions. Experimental results show that the proposed method outperforms existing methods across multiple evaluation metrics. The overall classification accuracy (OA) reaches 90.85%, the average accuracy (AA) reaches 91.67%, and the Kappa coefficient reaches 87.77%. The method shows good stability and adaptability under different sample sizes. The MetaMamba model can achieve high-precision classification of desert rangeland vegetation species under few-shot conditions, providing an effective technical approach for ecological monitoring and rangeland resource management.",
  "abstract_shown": true,
  "affiliations": [
    "Intelligent Manufacturing College, Hohhot Polytechnic University, Hohhot, Inner Mongolia, China",
    "College of Mechanical Engineering, Hebei Petroleum University of Technology, Chengde, Hebei, China",
    "College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China",
    "School of Mechanical Engineering, Guiyang University, Guiyang, Guizhou, China",
    "Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Fei Hao"
    },
    {
      "affs": [
        1
      ],
      "name": "Xinchao Gao"
    },
    {
      "affs": [
        2
      ],
      "name": "Tao Zhang"
    },
    {
      "affs": [
        3
      ],
      "name": "Jianmin Du"
    },
    {
      "affs": [
        4
      ],
      "name": "Shengli Wang"
    },
    {
      "name": "Xiaoyan He"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b2k191d5y5c4d.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0352744",
  "fulltext_url": null,
  "id": "b2k191d5y5c4d",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0352744",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2214|Remote-Sensing Image Classification"
    ],
    [
      "plos",
      "/Biology and life sciences/Ecology/Community ecology/Community structure"
    ],
    [
      "plos",
      "/Biology and life sciences/Ecology/Ecosystems/Deserts"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Neural networks"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning"
    ],
    [
      "plos",
      "/Computer and information sciences/Geoinformatics/Remote sensing imagery"
    ],
    [
      "plos",
      "/Computer and information sciences/Neural networks"
    ],
    [
      "plos",
      "/Earth sciences/Geography/Geoinformatics/Remote sensing imagery"
    ],
    [
      "plos",
      "/Ecology and environmental sciences/Ecology/Community ecology/Community structure"
    ],
    [
      "plos",
      "/Ecology and environmental sciences/Ecology/Ecosystems/Deserts"
    ],
    [
      "plos",
      "/Ecology and environmental sciences/Terrestrial environments/Deserts"
    ],
    [
      "plos",
      "/Engineering and technology/Remote sensing"
    ],
    [
      "plos",
      "/Engineering and technology/Remote sensing/Remote sensing imagery"
    ],
    [
      "plos",
      "/Engineering and technology/Technology development/Prototypes"
    ],
    [
      "plos",
      "/Research and analysis methods/Imaging techniques/Remote sensing imagery"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Mathematical functions/Convolution"
    ]
  ],
  "openalex": "W7169711967",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0352744&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-31",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/b2k191d5y5c4d.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:02Z",
      "native_id": "10.1371/journal.pone.0352744",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0352744"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:ecology",
      "subfield": "ecology"
    }
  ],
  "title": "MetaMamba: Meta-learning with mamba for few-shot vegetation species classification using UAV-based hyperspectral imagery in desert rangeland",
  "title_text": "MetaMamba: Meta-learning with mamba for few-shot vegetation species classification using UAV-based hyperspectral imagery in desert rangeland",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:02Z",
  "url": "https://research.draconitic.com/paper/b2k191d5y5c4d",
  "venue": "PLOS One",
  "versions": []
}