| Yazarlar (3) |
Doç. Dr. Caner ÖZCAN
Karabük Üniversitesi, Türkiye |
Okan Ersoy
Türkiye |
|
İskender Ülgen Oğul
Türkiye |
| Özet |
| Classification of a synthetic aperture radar (SAR) image is an essential process for SAR image analysisand interpretation. Recent advances in imaging technologies have allowed data sizes to grow, and a large number ofapplications in many areas have been generated. However, analysis of high-resolution SAR images, such as classification,is a time-consuming process and high-speed algorithms are needed. In this study, classification of high-speed denoisedSAR image patches by using Apache Spark clustering framework is presented. Spark is preferred due to its powerfulopen-source cluster-computing framework with fast, easy-to-use, and in-memory analytics. Classification of SAR imagesis realized on patch level by using the supervised learning algorithms embedded in the Spark machine learning library.The feature vectors used as the classifier input are obtained using gray-level cooccurrence matrix which is chosen toquantitatively evaluate textural parameters and representations. SAR image patches used to construct the featurevectors are first applied to the noise reduction algorithm to obtain a more accurate classification accuracy. Experimentalstudies were carried out using naive Bayes, decision tree, and random forest algorithms to provide comparative results,and significant accuracies were achieved. The results were also compared with a state-of-the-art deep learning method.TerraSAR-X images of high-resolution real-world SAR images were used as data. |
| Anahtar Kelimeler |
| Classification | machine learning | synthetic aperture radar | cluster computing | naive Bayes | decision tree | random forest |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale |
| Dergi Adı | TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES |
| Dergi ISSN | 1300-0632 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q4 |
| Makale Dili | İngilizce |
| Basım Tarihi | 01-2020 |
| Cilt No | 28 |
| Sayı | 1 |
| Sayfalar | 182 / 195 |
| Doi Numarası | 10.3906/elk-1904-7 |
| Makale Linki | http://dx.doi.org/10.3906/elk-1904-7 |
| Atıf Sayıları | |
| WoS | 5 |
| TRDizin | 1 |
| Google Scholar | 10 |
