深度学习在高分辨率遥感影像冬油菜提取中的应用

近年来，深度学习在基于高分辨率遥感影像的农作物种植信息提取领域应用广泛。本文充分利用油菜在盛花期的光谱特征，提出了基于深度学习理论的单时相高分辨率遥感影像油菜分布提取方法。以2016年湖北省沙洋县作为研究区域，获取油菜盛花时期高分一号（GF-1）影像，并以沙洋县为基础影像，通过手工标记制作油菜训练样本。设计两种深度学习框架模型，一种以卷积神经网络（CNN）为框架，构建一维卷积神经网络（1D-CNN）模型，第二种以循环神经网络（RNN）为框架，组合门控循环单元（GRU）模型，训练标准样本模型，完成油菜分类提取。最后，与传统支持向量机（SVM）、随机森林（RF）方法进行了结果对比。试验结果表明，本文设计的基于深度学习CNN和RNN模型提取的冬油菜空间分布精度和面积精度皆优于其他两种方法，为进一步实现冬油菜提取自动化提供试验基础。

Extraction of winter rapeseed from high-resolution remote sensing imagery via deep learning

Recently, deep learning technology has been widely used in the crop extraction from high-resolution remote sensing data. This paper exploits the use of the spectral characteristics of rapeseed in the flowering period, and proposes a rapeseed extraction method from single-phase high-resolution remote sensing image based on deep learning theory. The paper employs GF-1 satellite data during the rapeseed flowering period, Shayang City, Hubei Province, as the research data. Firstly, rapeseed training samples are annotated on the image by manual labeling. Then, two deep learning framework models are built, including a one-dimensional convolutional neural network (1D-CNN) and a recurrent neural network (RNN), to conduct the rapeseed extraction with the help of annotated training samples. Finally, the rapeseed extraction results are evaluated by comparing with traditional support vector machine (SVM) and random forest (RF) methods. The experimental results show that the spatial distribution accuracy and area accuracy of winter rapeseed based on deep learning CNN and RNN models designed in this paper are better than the other two methods, which provides guidance for automation of large area winter rapeseed extraction.