摘要:
本研究以滁州市全椒县为研究区,借助GEE平台,基于Sentinel-2卫星数据构建光谱特征、传统植被指数特征、红边植被指数特征、纹理特征等90个特征,选用基于随机森林的递归特征消除算法(random forest-recursive feature elimination,RF_RFE)、基于Relief拓展的Relief F算法、基于相似性的特征优选算法(correlation-based feature selection,CFS)结合随机森林分类器对农作物种植类型进行识别,探究不同的特征优选算法在农作物遥感识别中的效果优劣。在此基础上,进一步分析最佳特征优选算法在不同机器学习分类方法中的分类效果。研究表明: ①光谱特征在农作物识别中最为重要,其次是红边指数特征,纹理特征影响较小; ②基于RF_RFE特征优选方法的遥感识别结果精度最好,总体精度为92%,Kappa系数为0.89; ③在RF_RFE特征优选方法下,随机森林(random forest,RF)的Kappa系数比支持向量机分类(support vector machine,SVM)和最小距离分类(minimum distance classification,MDC)分别高0.01和0.41,说明基于多特征的RF_RFE特征优选方法结合RF算法可以有效提高农作物遥感识别精度和效率。
Abstract:
Focusing on Quanjiao County in Chuzhou City, this study determined 90 features, including spectral, traditional vegetation index, red-edge vegetation index, and texture features, from Sentinel-2 satellite data on the GEE platform. This study examined the effects of diverse feature optimization algorithms combined with a random forest classifier on identifying crop planting types in the study area. These algorithms included the random forest-recursive feature elimination (RF_RFE) algorithm, the Relief F algorithm based on Relief expansion, and the correlation-based feature selection (CFS) algorithm. On this basis, this study further analyzed the classification effects of the optimal feature optimization algorithm in various machine learning classification approaches. The study demonstrates that: ① Spectral features proved to be the most crucial for crop identification, followed by red-edge index features, and texture features manifested minimal effects; ② RF_RFE-based remote sensing identification results exhibited the highest accuracy, with overall accuracy of 92% and a Kappa coefficient of 0.89; ③ Under the RF_RFE feature optimization method, the RF’s Kappa coefficient was 0.01 and 0.41 higher than that of the support vector machine (SVM) and the minimum distance classification (MDC), respectively. This indicates that the RF_RFE feature optimization method based on multiple features, combined with the RF algorithm, can effectively enhance the accuracy and efficiency of remote sensing identification of crops.
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