农业机械学报2024，Vol.55Issue(9)：275-285,11.DOI:10.6041/j.issn.1000-1298.2024.09.023
基于多特征优化的PolSAR数据农作物精细分类方法
Crop Classification Based on PolSAR Data Using Multiple Feature Optimization
摘要
Abstract
Crop fine classification is of great significance in many fields such as agricultural resources survey and crop planting structure supervision.Polarimetric synthetic aperture radar(PolSAR)can effectively detect camouflage and penetrate masks,extract multiple scattering feature information,obtain continuous time series information covering the key climatic phases of crop growth,and effectively enhance the richness of crop remote sensing features,which is a unique advantage in crop classification.However,the introduction of multi-temporal phases and multi-features inevitably leads to a drastic increase in model arithmetic,which is not conducive to engineering applications.In view of the above problems,a multi-feature optimization-based approach for crop fine classification of PolSAR data was proposed,which firstly carried out multiple polarization target decomposition and parameter extraction of the PolSAR data in order to obtain multiple scattering features,and then a stacked sparse self-coding network based and ReliefF preferred method was used for feature enhancement and optimization to obtain the optimal set of features,and finally a convolutional neural network with two branching structures was constructed to fuse the features output from different convolutional depths to complete the high-precision classification of crops.Through the characterization of single-time-phase data,the preliminary classification experiments of single-time-phase data and the comparison experiments of combining classifiers with different feature sets of multi-time-phase data,it was proved that the method proposed can maximally extract the differential features between different crops under the premise of low-dimensional feature input,and accurately and efficiently realize the fine classification of crops,with the highest classification accuracy and Kappa coefficient reaching 97.69％and 97.24％,respectively.关键词
农作物分类/PolSAR/栈式稀疏自编码网络/ReliefF/卷积神经网络Key words
crop classification/PolSAR/stack sparse self-coding network/ReliefF/convolutional neural network分类
农业科技引用本文复制引用
郭交,王鹤颖,项诗雨,连嘉茜,王辉..基于多特征优化的PolSAR数据农作物精细分类方法[J].农业机械学报,2024,55(9):275-285,11.基金项目
国家自然科学基金项目(U22B2015)和陕西省重点研发计划项目(2024NC-ZDCYL-05-02) （U22B2015）
