  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.
