论文标题
基于时间特征从Sentinel-2数据中学习的陆地覆盖和作物分类改进，使用反复跨跨跨跨神经网络（R-CNN）
Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)
论文作者
论文摘要
诸如Sentinel-2提供的全球可用卫星图像的空间和时间分辨率越来越多，为研究人员提供了新的可能性，可为研究人员使用自由可用的多光谱光学图像，并具有deTametric的空间分辨率，并且更频繁地重新审视了远程感应应用，例如土地覆盖和作物分类（LC＆CC）（LC＆CC），农业监控和管理，环境监控和管理，环境监控，环境监控，环境监控。专用于农田映射的现有解决方案可以根据基于每个像素的基于对象和对象进行分类。但是，当更多种类的农作物被大规模考虑时，仍然具有挑战性。在本文中，基于复发性神经网络（RNN）与卷积神经网络（CNN）结合使用多个时空的Sentinel-2意大利中部地区的图像，开发和实施了一种基于像素的LC＆CC的新型和最佳的深度学习模型，并与卷积神经网络（CNN）结合使用，该图像具有多种农业系统。所提出的方法能够通过学习多个图像的时间相关性来自动提取特征提取，从而减少了手动特征工程和建模作物候位阶段。在这项研究中考虑了15种，包括主要农作物。我们还测试了其他广泛使用的传统机器学习算法进行比较，例如支持向量机SVM，Random Forest（RF），Kernal SVM和梯度增强机，也称为XGBoost。我们提出的Pixel R-CNN所达到的总体准确性为96.5％，与现有主流方法相比，这显示出相当大的提高。这项研究表明，基于Pixel R-CNN的模型提供了一种高度准确的方法来评估和使用时间序列数据进行多时间分类任务。
The increasing spatial and temporal resolution of globally available satellite images, such as provided by Sentinel-2, creates new possibilities for researchers to use freely available multi-spectral optical images, with decametric spatial resolution and more frequent revisits for remote sensing applications such as land cover and crop classification (LC&CC), agricultural monitoring and management, environment monitoring. Existing solutions dedicated to cropland mapping can be categorized based on per-pixel based and object-based. However, it is still challenging when more classes of agricultural crops are considered at a massive scale. In this paper, a novel and optimal deep learning model for pixel-based LC&CC is developed and implemented based on Recurrent Neural Networks (RNN) in combination with Convolutional Neural Networks (CNN) using multi-temporal sentinel-2 imagery of central north part of Italy, which has diverse agricultural system dominated by economic crop types. The proposed methodology is capable of automated feature extraction by learning time correlation of multiple images, which reduces manual feature engineering and modeling crop phenological stages. Fifteen classes, including major agricultural crops, were considered in this study. We also tested other widely used traditional machine learning algorithms for comparison such as support vector machine SVM, random forest (RF), Kernal SVM, and gradient boosting machine, also called XGBoost. The overall accuracy achieved by our proposed Pixel R-CNN was 96.5%, which showed considerable improvements in comparison with existing mainstream methods. This study showed that Pixel R-CNN based model offers a highly accurate way to assess and employ time-series data for multi-temporal classification tasks.
