DenseNet-based ensemble network for land cover and land use classification of patch-based denoised SAR images
Yazarlar (4)
Arş. Gör. Elif Meşeci Zonguldak Bülent Ecevit Üniversitesi, Türkiye
Doç. Dr. Caner ÖZCAN Karabük Üniversitesi, Türkiye
Arş. Gör. Dilara Özdemir Karabük Üniversitesi, Türkiye
Muhammet Dilmaç
| Makale Türü | Özgün Makale (Diğer hakemli uluslarası dergilerde yayınlanan tam makale) | ||
| Dergi Adı | Arabian Journal of Geosciences | ||
| Dergi ISSN | 1866-7511 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | GeoRef Google Scholar Japanese Science and Technology Agency (JST) Naver Norwegian Register for Scientific Journals and Series | ||
| Makale Dili | İngilizce | Basım Tarihi | 10-2023 |
| Cilt / Sayı / Sayfa | 16 / 597 / 1–11 | DOI | 10.1007/s12517-023-11709-2 |
| Makale Linki | http://dx.doi.org/10.1007/s12517-023-11709-2 | ||
| UAK Araştırma Alanları |
Görüntü İşleme
Makine Öğrenmesi
Yapay Zeka
|
||
| Özet |
| Classification of synthetic aperture radar (SAR) images is very important for analyzing these images. The developing remote sensing allows many high-dimensional SAR images to be recorded and interpreted. However, due to the growth in data sizes, the features increase, and analyzing becomes difficult. Therefore, deep learning algorithms capable of automatic feature extraction are needed. This study proposes SAR DenseNet-based Ensemble Network (SARDE-Net), an ensemble deep learning network based on DenseNet architectures, for the classification task. The high-dimensional real-world SAR image was taken from the TerraSAR-X image archive. Before the SAR image is transferred to our model, it is split into 100 x 100 patches and categorized into five classes. In addition, Sparsity-Driven Despeckling (SDD) filter is applied for denoising to increase the capability of proposed method on patch … |
| Anahtar Kelimeler |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 2 |