Fast texture classification of denoised SAR image patches using GLCM on Spark
Yazarlar (3)
Doç. Dr. Caner ÖZCAN Karabük Üniversitesi, Türkiye
Okan Ersoy
Purdue University, Amerika Birleşik Devletleri
Purdue University, Amerika Birleşik Devletleri
İskender Ülgen Oğul
Izmir Yüksek Teknoloji Enstitüsü, Türkiye
Izmir Yüksek Teknoloji Enstitüsü, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Turkish Journal of Electrical Engineering and Computer Sciences (Q4) | ||
| Dergi ISSN | 1300-0632 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2020 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 27-01-2020 |
| Cilt / Sayı / Sayfa | 28 / 1 / 182–195 | DOI | 10.3906/elk-1904-7 |
| Makale Linki | http://dx.doi.org/10.3906/elk-1904-7 | ||
| UAK Araştırma Alanları |
Yapay Zeka
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| Özet |
| Classification of a synthetic aperture radar (SAR) image is an essential process for SAR image analysis and interpretation. Recent advances in imaging technologies have allowed data sizes to grow, and a large number of applications 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 denoised SAR image patches by using Apache Spark clustering framework is presented. Spark is preferred due to its powerful open-source cluster-computing framework with fast, easy-to-use, and in-memory analytics. Classification of SAR images is 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 to quantitatively evaluate textural parameters and representations. SAR image patches used to construct the feature vectors are first applied to the noise reduction algorithm to obtain a more accurate classification accuracy. Experimental studies 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 | Cluster computing | Decision tree | Machine learning | Naive Bayes | Random forest | Synthetic aperture radar |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 5 |
| Scopus | 7 |
| Google Scholar | 10 |