Sea-Land Clutter Classification Based on Graph Spectrum Features

Abstract

In this paper, an approach for radar clutter, especially sea and land clutter classification, is considered under the following conditions: the average amplitude levels of the clutter are close to each other, and the distributions of the clutter are unknown. The proposed approach divides the dataset into two parts. The first data sequence from sea and land is used to train the model to compute the parameters of the classifier, and the second data sequence from sea and land under the same conditions is used to test the performance of the algorithm. In order to find the essential structure of the data, a new data representation method based on the graph spectrum is utilized. The method reveals the nondominant correlation implied in the data, and it is quite different from the traditional method. Furthermore, this representation is combined with the support vector machine (SVM) artificial intelligence algorithm to solve the problem of sea and land clutter classification. We compare the proposed graph feature set with nine exciting valid features that have been used to classify sea clutter from the radar in other works, especially when the average amplitude levels of the two types of clutter are very close. The experimental results prove that the proposed extraction can represent the characteristics of the raw data efficiently in this application.

Keywords

• radar clutter classification
• graph feature extraction
• support vector machine
• average amplitude level