CROP CLASSIFICATION BASED ON A GAOFEN 1/WIDE-FIELD-VIEW TIME SERIES

Abstract

The accurate acquisition of crop planting area information allows for agricultural management departments to understand crop production information promptly. Concurrently, support vector machine (SVM) algorithms are unable to determine parameter combinations in remote sensing image crop classification to obtain optimal classification results. To solve this issue, this paper proposes an SVM that is optimized by the adaptive mutation particle swarm optimization algorithm. To test the algorithm, we undertook an experiment in Acheng District, Harbin City, Heilongjiang province, China, using Gaofen 1/wide-field-view satellite images to construct a time series for various vegetation indices. The SVM model with optimized parameters was compared with a traditional backpropagation neural network, a decision tree, and an SVM without parameter optimization. The experimental results verified that the improved SVM model obtained the highest classification accuracy.