关键词: 可见光遥感, 油菜倒伏, 粒子群特征优化, 集成学习

Abstract: To achieve the high-precision extraction of rapeseed lodging by visible remote sensing, this paper put forward a feature optimization and rapeseed lodging recognition algorithm based on particle swarm optimization-mutual information (PSO-MI) and stacking ensemble learning by the visible vegetation indices, principal component band texture features, and DSM of the experimental area. This paper proposed the particle swarm random loop search method, introduced the mutual information threshold adaptive strategy, and built the applicability function to screen the lodging characteristics of rapeseed. A stacking ensemble learning model based on optimized features was constructed using logistic regression (LR) meta learner combined with four base learners, namely, K-nearest neighbor (KNN), random forest (RF) and support vector machine (SVM), which realized the identification of lodging rapeseed and mapping, and effectively improved the accuracy of lodging rapeseed recognition. This study provides a target recognition method combining stochastic intelligent feature optimization and high-performance machine ensemble learning, which can provide a technical reference for remote sensing extraction of target features.

Key words: visible remote sensing, rapeseed lodging, particle swarm feature optimization, ensemble learning