Replacing human interpretation of agricultural land in Afghanistan with a deep convolutional neural network
Replacing human interpretation of agricultural land in Afghanistan with a deep convolutional neural network
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DOI：
10.1080/01431161.2020.1864059
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发表时间：
2021-04-18
影响因子：
3.4
通讯作者：
Waine, T. W.
中科院分区：
文献类型：
--
作者：
Hamer, A. M.;Simms, D. M.;Waine, T. W.
阿富汗的年度鸦片调查依赖于耗费时间的人工判读卫星图像，绘制潜在罂粟种植区的地图，以便进行统计抽样设计。深度卷积神经网络（CNN）通过编码本地上下文信息，在图像分类任务中表现出突破性的性能，在某些情况下优于训练有素的分析师。在这项研究中，我们研究了CNN的发展，以自动分类的农业从中等分辨率的卫星图像作为替代手动解释。对残差网络（ResNet50）CNN架构进行了培训和验证，以使用赫尔曼德省和坎大哈省的标记多季节灾害监测星座卫星图像（32米）划定农业区。研究了输入图像芯片大小、训练采样策略、高程数据和多季节图像的影响。表现最好的单年分类使用了33 x 33像素的输入芯片大小，有针对性的采样策略和迁移学习，从而实现了较高的整体准确率（94%）。包含海拔数据略微降低了性能（93%）。使用前两年的数据，多季节分类的总体准确率达到89%。仅需要25%的目标年训练样本来更新模型，以实现>94%的总体准确度。提出了一种使用CNN自动化农业口罩生产的数据驱动方法，以减轻人类解释的负担。使用新数据不断更新CNN模型的能力有可能显着提高多年来植被的自动分类。
Afghanistan's annual opium survey relies upon time-consuming human interpretation of satellite images to map the area of potential poppy cultivation for statistical sample design. Deep Convolutional Neural Networks (CNNs) have shown ground-breaking performance for image classification tasks by encoding local contextual information, in some cases outperforming trained analysts. In this study, we investigate the development of a CNN to automate the classification of agriculture from medium-resolution satellite imagery as an alternative to manual interpretation. The residual network (ResNet50) CNN architecture was trained and validated for delineating the agricultural area using labelled multi-seasonal Disaster Monitoring Constellation (DMC) satellite imagery (32 m) of Helmand and Kandahar provinces. The effect of input image chip size, training sampling strategy, elevation data, and multi-seasonal imagery were investigated. The best-performing single-year classification used an input chip size of 33 x 33 pixels, a targeted sampling strategy and transfer learning, resulting in high overall accuracy (94%). The inclusion of elevation data marginally lowered performance (93%). Multi-seasonal classification achieved an overall accuracy of 89% using the previous two years' data. Only 25% of the target year's training samples were necessary to update the model to achieve >94% overall accuracy. A data-driven approach to automate agricultural mask production using CNNs is proposed to reduce the burden of human interpretation. The ability to continually update CNN models with new data has the potential to significantly improve automatic classification of vegetation across years.