Improvements in Earth observation by satellites allow for imagery of ever higher temporal and spatial resolution. Leveraging this data for agricultural monitoring is key for addressing environmental and economic challenges. Current methods for crop segmentation using temporal data either rely on annotated data or are heavily engineered to compensate the lack of supervision. In this paper, we present and compare datasets and methods for both supervised and unsupervised pixel-wise segmentation of satellite image time series (SITS). We also introduce an approach to add invariance to spectral deformations and temporal shifts to classical prototype-based methods such as K-means and Nearest Centroid Classifier (NCC). We show this simple and highly interpretable method leads to meaningful results in both the supervised and unsupervised settings and significantly improves the state of the art for unsupervised classification of agricultural time series on four recent SITS datasets.

翻译：卫星对地观测技术的改进使得获取具有更高时间和空间分辨率的图像成为可能。利用这些数据进行农业监测是应对环境和经济挑战的关键。当前基于时间序列的作物分割方法要么依赖标注数据，要么需要大量人工设计来弥补监督的不足。本文介绍并对比了用于卫星图像时间序列（SITS）有监督与无监督像素级分割的数据集及方法。我们还提出了一种方法，为经典的基于原型的方法（如K-means和最近质心分类器（NCC））增加对光谱变形和时间偏移的不变性。我们证明，这种简单且高度可解释的方法在有监督和无监督场景下均能产生有意义的结果，并在四个最新的SITS数据集上显著提升了农业时间序列无监督分类的当前最优性能。