面向地块的农作物遥感分类研究进展

A review of per-field crop classification using remote sensing

农作物遥感分类是农作物面积监测的核心问题,对于进一步开展农作物长势、产量等专题监测具有重要意义。与同质像元聚类得到的对象相比,地块数据包含了更为精确的位置和面积信息,被越来越多地应用于农作物遥感分类。首先,系统总结了面向地块农作物遥感分类在理论、方法和实践中取得的进展;然后,分析了该方法目前存在的问题;最后,对未来的发展趋势进行了展望。研究认为,数字化和影像分割是获取地块数据的主要途径,陆续发布的全国地块数据集也给面向地块农作物遥感分类带来了新的契机;将面向地块的农作物遥感分类策略分为考虑地块整体特征和以像元为基础2种,并总结了遥感分类特征和分类方法取得的进展;在未来一段时间,多源数据的应用、地块边界检测技术的发展、分类特征的挖掘以及遥感分类运行化能力的提高将是面向地块农作物遥感分类的重要研究内容。

Crop classification using remote sensing is the key to monitoring crop planting acreage and has great significance in further thematic monitoring.As field contains more accurate information of location and acreage than object which is the result of clustering similar pixels,it has been applied to crop classification using remote sensing increasingly.This paper summarizes the progress of per-field crop classification using remote sensing systematically,including its theories,methods and applications.Furthermore,a series of problems are analyzed and future study directions are viewed.Studies show that digitalization and image segmentation are the main approach to obtaining field boundary and more nationwide field database and bringing per-field classification a new opportunity.The strategies of per-field classification can be divided into two categories:using field features as input for the classifier and assigning field class based on per-pixel classification.The progress of features and classifiers in classification with remote sensing data are summarized further.It is indicated that combined application of multi-source data,development of field boundary detection,new features selection and improving implementation capacity of remote sensing image classification will be the crucial issues in per-field classification using remote sensing.