Multi-modal remote sensory learning for multi-objects over autonomous devices
Several performance measures were calculated to assess the efficacy of the suggested technique, including accuracy, precision, recall, error, and F1-Score. The assessment findings show a remarkable recognition rate of around 97.90% and 98.90%, on the AID and the UC Merced Land datasets, respectively.
Authors
- Naif Almudawi
- Abdulwahab Alazeb (ORCID: https://orcid.org/0000-0001-9661-7440)
- Asaad Algarni (ORCID: https://orcid.org/0000-0002-0817-5775)
- Ahmad Jalal (ORCID: https://orcid.org/0009-0000-8421-8477)
- Hanan Aljuaid (ORCID: https://orcid.org/0000-0001-6042-0283)
- Hui Liu (ORCID: https://orcid.org/0009-0005-7201-6903)
- Yahay AlQahtani
- Aysha Naseer
Institutions
- Princess Nourah bint Abdulrahman University (SA)
- Northern Border University (SA)
- University of Bremen (DE)
- Korea University (KR)
- Nanjing University of Information Science and Technology (CN)
- Guodian Institute of Science and Technology (CN)
- Najran University (SA)
- King Khalid University (SA)
- Air University (PK)
Publication Details
- Journal
- Frontiers in Bioengineering and Biotechnology
- Published
- 2025-05-20
- DOI
- https://doi.org/10.3389/fbioe.2025.1430222
- Primary Topic
- Remote-Sensing Image Classification
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Deutsche Forschungsgemeinschaft
- Princess Nourah Bint Abdulrahman University
- Najran University
- King Khalid University
- Universität Bremen