论文标题
通过机器学习方法进行谷物表面分类
Grain Surface Classification via Machine Learning Methods
论文作者
论文摘要
在这项研究中，分析了雷达信号，以使用机器学习方法对晶粒表面类型进行分类。使用18-40 GHz之间的向量网络分析仪记录雷达反向散射信号。总共收集了5681个扫描信号的测量。提出的方法框架由两个部分组成。一阶统计特征是通过在框架第一部分中应用快速傅立叶变换（FFT），离散余弦变换（DCT），离散小波变换（DWT）获得的。这些特征的分类过程是使用支持向量机（SVM）进行的。在提议的框架的第二部分中，通过在信号上施加短时傅立叶变换（STFT）来获得复杂形式的二维矩阵。获得了灰度级的共发生矩阵（GLCM）和灰级跑步矩阵（GLRLM），并完成了特征提取过程。使用DVM进行分类过程。应用10-K交叉验证。使用STFT+GLCM+SVM实现了最高的性能。
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.
