Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis
Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis
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DOI：
10.1016/j.rse.2017.10.005
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发表时间：
2018-01-01
影响因子：
13.5
通讯作者：
Csillik, Ovidiu
中科院分区：
文献类型：
--
作者：
Belgiu, Mariana;Csillik, Ovidiu
有效的耕地测绘方法是实施可持续农业做法和定期监测作物的基本条件。全球可用的卫星图像，如哨兵2号卫星提供的图像，其空间和时间分辨率不断提高，为以随时可用的矢量数据格式生成关于现有作物类型的准确数据集创造了新的可能性。基于高分辨率遥感数据的耕地制图的现有解决方案主要集中在基于像素的时间序列数据分析上。本文评估了如何使用哨兵2时间序列的时间加权动态时间规整（TWDTW）的方法时，适用于基于像素和基于对象的分类在三个不同的研究领域（罗马尼亚，意大利和美国）的各种作物类型。将分类输出与随机森林（RF）为基于像素和基于对象的图像分析单元产生的分类输出进行比较。还评估了这两种方法对训练样本的灵敏度。基于对象的TWDTW在所有三个研究领域都优于基于像素的TWDTW，总体准确率在78.05%和96.19%之间;它也被证明在计算时间方面更有效。TWDTW在罗马尼亚和意大利取得了与RF相当的分类结果，但RF在美国取得了更好的结果，在美国，分类的作物呈现出高的类内光谱变异性。此外，TWDTW被证明是不太敏感的训练样本。这在训练样本的投入有限的领域是一项重要的资产。
Efficient methodologies for mapping croplands are an essential condition for the implementation of sustainable agricultural practices and for monitoring crops periodically. The increasing spatial and temporal resolution of globally available satellite images, such as those provided by Sentinel-2, creates new possibilities for generating accurate datasets on available crop types, in ready-to-use vector data format. Existing solutions dedicated to cropland mapping, based on high resolution remote sensing data, are mainly focused on pixel-based analysis of time series data. This paper evaluates how a time-weighted dynamic time warping (TWDTW) method that uses Sentinel-2 time series performs when applied to pixel-based and object-based classifications of various crop types in three different study areas (in Romania, Italy and the USA). The classification outputs were compared to those produced by Random Forest (RF) for both pixel- and object-based image analysis units. The sensitivity of these two methods to the training samples was also evaluated. Object-based TWDTW outperformed pixel-based TWDTW in all three study areas, with overall accuracies ranging between 78.05% and 96.19%; it also proved to be more efficient in terms of computational time. TWDTW achieved comparable classification results to RF in Romania and Italy, but RF achieved better results in the USA, where the classified crops present high intra-class spectral variability. Additionally, TWDTW proved to be less sensitive in relation to the training samples. This is an important asset in areas where inputs for training samples are limited.