Analysis of winter wheat recognition ability based on multiphase Sentinel-2A data

Abstract

Effective and dynamic recognition of winter wheat has important implications for the development of agriculture in In this paper, we proposed a method for winter wheat identification using particle swarm optimization-support vector machine (PSO-SVM) model and multi-temporal Sentinel-2A image. The eigenvector combination based on spectral information and the eigenvector combination based on texture information were constructed by using different phenological periods of winter wheat. The winter wheat was identified and extracted by PSO-SVM. The extraction accuracy under different feature band combinations was compared and analyzed. The results showed that PSO-SVM had higher accuracy than traditional SVM. Using PSO-SVM, the optimal combination was multi-temporal spectral and mean texture information combination and its classification accuracy was 91.25%. This paper provides a theoretical basis for the future use of Sentinel-2A data to extract other crop information.