农业机械学报2026，Vol.57Issue(20)：41-53,13.DOI:10.6041/j.issn.1000-1298.2026.20.004
基于超分辨率重建的农田遥感影像分类方法
Method for Classifying Farmland Remote Sensing Images Based on Super-resolution Reconstruction
摘要
Abstract
Large-scale agricultural land classification mapping plays an irreplaceable role in ensuring food security,optimizing irrigation management,protecting the ecological environment,supporting the construction of high-standard farmland,and promoting the sustainable development of agricultural resources.However,the limited spatial resolution of freely available satellite data(e.g.,Sentinel-2)hinders precise field-level observation,while high-resolution imagery remains costly.The potential of 16×super-resolution(SR)reconstruction to overcome this limitation was investigated.High-magnification multi-scale progressive growing generative adversarial network(HM-PGGAN),a novel network integrating multi-scale progressive generation and attention mechanisms,was proposed to enhance the structural fidelity of high-magnification SR reconstruction from Sentinel-2 data.The research evaluated not only the classification improvement from SR-reconstructed imagery but also the synergistic effect of combining it with GLCM-based texture features,using both support vector machine(SVM)and random forest(RF)classifiers.Comparative experiments on typical land cover types(roads,buildings,bare soil)showed that 16×SR reconstruction with texture features boosted the overall classification accuracy(OA)for downsampled 40 m Sentinel-2 imagery from 95.45%(SVM)and 96.76%(RF)to 98.19%(SVM)and 98.16%(RF).In a cross-platform SR experiment,OA was improved from 77.79%(SVM)and 78.50%(RF)to 93.28%(SVM)and 93.77%(RF).The results demonstrated that while both SRR and the integration of texture features as ancillary spatial information enhanced classification accuracy,the responses varied significantly across different land cover types.The research result can provide a novel technical pathway for high-precision remote sensing classification of agricultural planting areas.关键词
超分辨率重建/生成对抗网络/种植区分类/跨平台影像重建Key words
super-resolution reconstruction/generative adversarial network/planting area classification/cross-platform image reconstruction分类
农业科技引用本文复制引用
韩昊,冯子怡,刘永升,许胜,杜文,许童羽..基于超分辨率重建的农田遥感影像分类方法[J].农业机械学报,2026,57(20):41-53,13.基金项目
国家重点研发计划项目(2024YFD500602-2) （2024YFD500602-2）
