TY  - JOUR
TI  - Fast texture classification of denoised SAR image patches using GLCM on Spark
AB  - 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.
AU  - ersoy, okan
AU  - OĞUL, İskender Ülgen
AU  - Ozcan, Caner
DO  - 10.3906/elk-1904-7
PY  - 2020
JO  - Turkish Journal of Electrical Engineering and Computer Sciences
VL  - 28
IS  - 1
SN  - 1300-0632
SP  - 182
EP  - 195
DB  - TRDizin
UR  - http://search/yayin/detay/334578
ER  -