中高分辨率影像种植结构遥感提取方法比选研究OA

In areas with complex planting structures and fragmented plots,conducting fine-scale monitoring of crop planting structures using medium and high-resolution remote sensing images still faces challenges such as mixed pixel interference and spectral similarity among crops.To systematically evaluate the applicability of different models in such scenarios,Xinjin District of Chengdu City was selected as the study area.Based on multi-source time-series images from Sentinel-2 and Landsat 8/9,the spectral-temporal characteristics of overwintering(small spring)and spring-sown(large spring)crops were analyzed across the blue,green,red,and near-infrared bands.Subsequently,four models-TempCNN,LSTM,Transformer,and Random Forest(RF)-were constructed to evaluate and compare their performance in planting structure extraction.The results show that TempCNN not only has high patch integrity and good spatial continuity in the identification of major crops such as wheat,rapeseed,rice,and corn,but also has significantly higher overall classification accuracy than other models.Although LSTM and Transformer perform well in some crop categories and have relatively complete patch recognition,their overall accuracy is still lower than that of TempCNN.In contrast,the extraction performance of RF is significantly inferior to the other three deep learning models.In conclusion,among the evaluated models,TempCNN performs best in extracting planting structures using medium-and high-resolution remote sensing images in areas with fragmented plots and complex planting structures.