Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/4092
Title: Grain Surface Classification via Machine Learning Methods
Authors: Yiğit, Enes
Özkaya, Umut
Duysak, Huseyin
Keywords: Radar
Measurement
Machine Learning
Classification
Radar
Ölçüm
Makine Öğrenmesi
Sınıflama
Abstract: In this study, radar signals were analyzed to classify grain surface types by using machine learning methods. Radar backscatter signals were recorded using a vector network analyzer between 18-40 GHz. A total of 5681 measurements of A scan signals were collected. The proposed method framework consists of two parts. First Order Statistical features are obtained by applying Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) on backscatter signals in the first part of the framework. Classification process of these features was carried out with Support Vector Machine (SVM). In the second part of the proposed framework, two dimensional matrices in complex form were obtained by applying Short Time Fourier Transform (STFT) on the signals. Gray-Level Co-Occurrence Matrix (GLCM) and Gray-Level Run-Length Matrix (GLRLM) were obtained and feature extraction process was completed. Classification process was carried out with DVM. 10-k cross validation was applied. The highest performance was achieved with STFT+GLCM+SVM.
URI: https://doi.org/10.31590/ejosat.802719
https://search.trdizin.gov.tr/yayin/detay/1136048
https://hdl.handle.net/20.500.13091/4092
ISSN: 2148-2683
Appears in Collections:TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collections

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