农业机械学报2024，Vol.55Issue(9)：262-274,13.DOI:10.6041/j.issn.1000-1298.2024.09.022
基于不同时间尺度与特征优选的黄淮海平原冬小麦识别
Identification of Winter Wheat in Huang-Huai-Hai Plain Based on Different Time Scales and Feature Preference
摘要
Abstract
Monitoring regional crop acreage accurately and promptly is critical for ensuring food security and promoting sustainable agricultural development in China.The Google Earth Engine(GEE)cloud platform,along with fused Sentinel-1 SAR radar and Sentinel-2 SR surface reflectance imagery were employed to classify winter wheat in 2021 within the Huang-Huai-Hai Plain.The Sentinel time series data were synthesized and smoothed across various temporal scales,and a prioritization of polarization features,spectral features,vegetation index,harmonic coefficients,and textural features were conducted to explore their impacts on the accuracy and generalization ability of winter wheat identification in the region.The results showed that the feature optimization process improved the classification accuracy of the model,and the spectral features were the most significant,followed by harmonic coefficients,polarization,and textural features.Reducing the time scale of image sequences led to higher classification accuracy,with overall accuracies of 95.4％,95.6％,and 96.4％for 30 d,20 d and 10 d scales,respectively.However,this also resulted in a decrease in generalization ability,with corresponding scores of 0.935,0.919,and 0.918.Shorter time scales captured finer details of ground features,achieving higher classification accuracy but showing less adaptability to data variations.Moreover,the model's generalization ability demonstrated a spatial pattern of'the closer it was,the more relevant they were'.The identification of winter wheat areas using the GEE platform and Sentinel imagery was highly accurate,with overall accuracy and F1 scores of the confusion matrix exceeding 90％,and classification results were highly consistent in spatial detail with high-resolution images.Furthermore,the estimated areas of winter wheat showed a strong correlation with official municipal statistics(coefficient of determination R2＞0.9).关键词
冬小麦/遥感识别/Google Earth Engine/随机森林/时间尺度/特征优选Key words
winter wheat/remote sensing imagery identification/Google Earth Engine/random forest/time scale/feature preference分类
农业科技引用本文复制引用
周俊伟,冯浩,董勤各..基于不同时间尺度与特征优选的黄淮海平原冬小麦识别[J].农业机械学报,2024,55(9):262-274,13.基金项目
国家自然科学基金项目(51879224、51609237) （51879224、51609237）
