迁移深度卷积神经网络模型秋粮作物泛化识别

Transferring deep convolutional neural network models for generalization mapping of autumn crops

深度卷积神经网络DCNN (Deep Convolutional Neural Networks)具有“端到端”、高效提取多尺度语义特征的优点,被广泛应用于遥感农作物识别中,取得了重要的进展。但深度学习模型需要大量标签样本,获取费时费力、成本高,限制了其广泛使用。本文基于模型迁移学习策略,将美国农业部统计署作物数据层CDL(Cropland Data Layer)作为作物标签数据,利用伊利诺伊州和印第安州的作物生长季Landsat OLI卫星影像训练2016年—2019年4个单年份模型和2016年—2017年、2016年—2018年两个多年份的U-net模型,将模型直接在美国3个测试区和中国黑龙江省黑河市的2016年—2020年迁移泛化分类。研究结果表明:1)基于CDL训练的U-net模型能够在美国本土推广应用,美国3个测试区的2016年—2020年的时间泛化总体精度在80%以上,玉米识别精度高于大豆,说明了模型在空间上具有很好的迁移能力。(2)对于黑河市秋粮,多年玉米识别总体精度平均高出大豆3%,原因是玉米种植地块更为规整、种植规模更大,单年份秋粮识别总体精度69%—79%,但多年份模型要优于单年份模型,这可能是随标签样本数量增多提升了训练样本的代表性,将中国与美国秋粮种植的差异通过样本数量的增加得以弥补,模型迁移到中国黑河区域的精度均低于美国本土,这是由于洲际间气候、作物物候、种植农业景观等差异导致遥感特征不一致,导致模型泛化能力降低。

Deep Convolutional Neural Networks(DCNNs)have been increasingly applied in remote sensing crop recognition due to their“end-to-end”advantages and efficient extraction of shallow shape details and deep semantic features.However,deep learning models require a large number of labeled samples,which are time-consuming,labor-intensive,and costly to obtain,limiting the 2016—2020 period.The U-net model based on CDL training can be popularized and applied in the United States.First,the overall accuracy of time generalization in the three test areas in the United States from 2016 to 2020 is more than 80%,and the recognition accuracy of corn is higher than that of soybeans.Deep learning models have good transferability in space.Second,for autumn grain in Heihe City,the average recognition accuracy of corn for many years is 3%higher than that of soybean.This is because the corn planting plot is more regular and the planting scale is higher than that of soybean;the overall accuracy of autumn grain identification in a single year is between 69%and 79%.The year classification model is better than the single-year classification model,which may be because the representativeness of the training samples is enhanced with the increase of the number of labeled samples,and the difference in autumn grain planting between China and the United States can be compensated by the expansion of the number of samples.However,the model is migrated to the Heihe region of China.The accuracy of the models is lower than that of the continental United States,which is due to the inconsistency of remote sensing response characteristics due to differences in intercontinental climate and crop planting habits.These,in turn,reduce the generalization performance of the model.The DCNN model is better than random forest algorithm because of the training process driven by big data.The principle of transferring the basic trained crop classification model to map crop distribution timely and accurately has broad prospects for application across a large scale of time and space.The consistency of remote sensing features and phenology of the crops of the test area compared to the training data are fundamental factors that must be carefully considered,as these determine the success of high crop mapping performance.Therefore,it is essential to analyze the prerequisites when transferring the model to other places.