Insect Mass Estimation Based on Radar Cross Section Parameters and Support Vector Regression Algorithm

Abstract

Radar cross section (RCS) parameters of insect targets contain information related to their morphological parameters, which are helpful for the identification of migratory insects. Several morphological parameter estimation methods have been presented. However, most of these estimations are performed based on polynomial fitting methods, using only one or two parameters, which may limit the estimation accuracy. In this paper, a new insect mass estimation method is proposed based on support vector regression (SVR). Several RCS parameters were extracted for the estimation of insect mass. Support vector regression based on recursive feature elimination (SVRRFE) was used to obtain the optimal feature subset. Specifically, a dataset including 367 specimens was included to evaluate the performance of the proposed method. Fifteen features were extracted and ranked. The optimal feature subset contained six features and the optimal mass estimation accuracy was 78%. Additionally, traditional insect mass estimation methods were analyzed for comparison. The results prove that the proposed method is more effective and accurate for insect mass estimation. It needs to be emphasized that the poor number of experimental insects available may limit the further improvement of estimation accuracy.

Keywords

• insect
• radar cross section
• feature extraction
• parameter estimation
• support vector regression