Optimization based manifold embedding for hyperspectral image classification and visualization
Yazarlar (3)
Dr. Öğr. Üyesi Mehmet Zahid YILDIRIM Karabük Üniversitesi, Türkiye
Doç. Dr. Caner ÖZCAN Karabük Üniversitesi, Türkiye
Okan Ersoy
College Of Engineering, Amerika Birleşik Devletleri
College Of Engineering, Amerika Birleşik Devletleri
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Remote Sensing Letters (Q3) | ||
| Dergi ISSN | 2150-704X Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 12-2021 |
| Cilt / Sayı / Sayfa | 12 / 11 / 1158–1166 | DOI | 10.1080/2150704X.2021.1974118 |
| Makale Linki | http://dx.doi.org/10.1080/2150704x.2021.1974118 | ||
| UAK Araştırma Alanları |
Yapay Zeka
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| Özet |
| Remote sensing and interpretation of hyperspectral images are becoming an increasingly important field of research. High dimensional hyperspectral images consist of hundreds of bands and reflect the properties of different materials. The need for more detail about objects and the improvement of sensor resolutions have resulted in the generation of higher size hyperspectral data. Many years of research have shown that there are many difficulties in the pre-processing of these data due to their high dimensionality. Recent studies have revealed that manifold learning techniques are a very important solution to this problem. However, as the complexity of the data increases, the performance of these methods cannot reach a sufficient level. This letter proposes a particle swarm-based multidimensional field embedding method inspired by the force field formulation to increase the performance. Detailed comparative … |
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BM Sürdürülebilir Kalkınma Amaçları
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| Web of Science | 1 |
| Scopus | 2 |
| Google Scholar | 3 |