MLRSNet: capsule networks-based multi-label hyperspectral image retrieval

Gültekin A., Diri S., Sayar A., Topçu M., Dede A.

SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES, cilt.50, sa.4, 2025 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 50 Sayı: 4
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1007/s12046-025-02929-1
  • Dergi Adı: SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Kocaeli Üniversitesi Adresli: Evet

Özet

With the growing volume and complexity of remote sensing data, effective multi-label image retrieval has become a critical challenge in hyperspectral image analysis. While deep learning techniques have significantly enhanced classification performance, retrieval models that preserve spatial hierarchies and semantic co-occurrence remain underexplored. In the present study, we propose MLRSNet, a capsule network-based architecture tailored for multi-label hyperspectral image retrieval. MLRSNet integrates capsule routing and a reconstruction loss mechanism to maintain part-whole relationships and improve discriminative feature learning in complex scenes. For evaluation, we employed two benchmark datasets, a publicly available relabeled version of the UC Merced Land Use dataset, constructed using a semi-supervised graph-theoretic method in prior work, and the Ankara hyperspectral imaging (HSI) dataset, which contains native multi-label annotations. These datasets enabled the evaluation of retrieval performance in realistic multi-label scenarios. Experimental results across five random train-test splits show that MLRSNet consistently outperforms conventional CNN-based approaches in terms of accuracy, precision and recall.