{
  "abstract": "Hyperspectral images (HSI) play a vital role in remote sensing applications such as surveillance and precision agriculture, yet they face challenges including limited labeled samples and class imbalance. This paper presents a new method for HSI classification by integrating spectral band selection with advanced deep learning techniques. First, we propose an optimized band selection method alongside data augmentation using crop-and-resize transformations to enhance feature diversity. Second, we introduce a hybrid convolution-transformer framework that combines Vision Transformer (ViT) and 2D-CNN architectures, supplemented with a sequence aggregation layer to mitigate overfitting on small datasets. This hybrid model effectively captures both spatial and spectral features while preserving critical spectral details. Finally, we employ a support vector machine for robust classification. Extensive experiments on three widely used HSI datasets demonstrate that our proposed ViT-CNN-BS framework achieves superior classification accuracy compared to state-of-the-art few-shot learning methods and traditional approaches. Beyond achieving higher accuracy, our approach also reduces computational complexity, making it suitable for resource-constrained applications. The findings highlight the potential of hybrid deep learning frameworks for advancing remote sensing analytics, with possible extensions to environmental monitoring, disaster management, and precision agriculture.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Eman N. Abdelhafez"
    },
    {
      "name": "Ahmed Hagag",
      "orcid": "0000-0003-2631-1846"
    },
    {
      "name": "Tamer A. Abassy"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0mfp3f3tgnhxj.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-72102-z",
  "fulltext_url": null,
  "id": "0mfp3f3tgnhxj",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-72102-z",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2214|Remote-Sensing Image Classification"
    ]
  ],
  "openalex": "W7221518213",
  "pdf_url": "https://www.nature.com/articles/s41598-026-72102-z.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-12",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/0mfp3f3tgnhxj.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:26Z",
      "native_id": "10.1038/s41598-026-72102-z",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-72102-z"
    }
  ],
  "subjects": [],
  "title": "Hybrid transformer and CNN for hyperspectral image classification with spectral band selection",
  "title_text": "Hybrid transformer and CNN for hyperspectral image classification with spectral band selection",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:26Z",
  "url": "https://research.draconitic.com/paper/0mfp3f3tgnhxj",
  "venue": "Scientific Reports",
  "versions": []
}