Deep Learning Classification for Crop Types in North Dakota
Deep Learning Classification for Crop Types in North Dakota
复制标题
北达科他州农作物类型的深度学习分类
DOI：
10.1109/jstars.2020.2990104
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发表时间：
2020-05
影响因子：
5.5
通讯作者：
Ziheng Sun;L. Di;Hui Fang;A. Burgess
中科院分区：
文献类型：
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作者：
Ziheng Sun;L. Di;Hui Fang;A. Burgess
近年来，农业遥感界正在努力利用人工智能（AI）的力量。一个重要的主题是使用人工智能使作物的测绘更加准确、自动和快速。本文提出了一种使用深度神经网络（DNN）的分类工作流程，从北达科他州的Landsat图像中生成高质量的当季作物地图。我们使用农业部门的历史作物地图和北达科他州的地面测量作为训练数据集。创建处理工作流是为了使繁琐的预处理、训练、测试和后处理工作流自动化。我们在新图像上测试了这种混合溶液，并在玉米、大豆、大麦、春小麦、干豆、甜菜和苜蓿等主要作物上获得了准确的结果。所有三个测试区域的所有土地类型（包括非耕地）的像素总体精度都超过82%，与美国农业部农田数据层的精度相同。DNN地图的纹理更一致，噪音更少，阅读起来更舒服。我们发现DNN在识别大型农田方面比识别北达科他州分散的湿地和郊区要好。在多年和多个月的多个场景上训练的模型比仅在单个场景、单个月或单个年上训练的任何模型产生更高的准确性。这些结果表明，DNN可以为北达科他州大农场的主要作物提供可靠的季节图，并可以为分散湿地田的次要作物提供相对准确的参考。
Recently, agricultural remote sensing community has endeavored to utilize the power of artificial intelligence (AI). One important topic is using AI to make the mapping of crops more accurate, automatic, and rapid. This article proposed a classification workflow using deep neural network (DNN) to produce high-quality in-season crop maps from Landsat imageries for North Dakota. We use historical crop maps from the agricultural department and North Dakota ground measurements as training datasets. Processing workflows are created to automate the tedious preprocessing, training, testing, and postprocessing workflows. We tested this hybrid solution on new images and received accurate results on major crops such as corn, soybean, barley, spring wheat, dry beans, sugar beets, and alfalfa. The pixelwise overall accuracy in all three test regions is over 82% for all land types (including noncrop land), which is the same level of accuracy as the U.S. Department of Agriculture Cropland Data Layer. The texture of DNN maps is more consistent with fewer noises, which is more comfortable to read. We find DNN is better on recognizing big farmlands than recognizing the scattered wetlands and suburban regions in North Dakota. The model trained on multiple scenes of multiple years and months yields higher accuracy than any of the models trained only on a single scene, a single month, or a single year. These results reflect that DNN can produce reliable in-season maps for major crops in North Dakota big farms and could provide a relatively accurate reference for the minor crops in scattered wetland fields.