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 study different levels of supervision and show this simple and highly interpretable method achieves the best performance in the low data regime 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进步使得图像的时间和空间分辨率不断提高。利用此类数据进行农业监测对于应对环境与经济挑战至关重要。当前基于时序数据的作物分割方法要么依赖于标注数据，要么需要大量人工设计以弥补监督信息的缺失。本文提出并比较了用于卫星图像时间序列（SITS）监督与非监督逐像素分割的数据集与方法。我们还提出一种方法，为经典的原型分类方法（如K-means和最近质心分类器）增加对光谱形变与时序偏移的不变性。我们研究了不同监督水平，并证明这种简单且高度可解释的方法在低数据量场景下取得了最佳性能，同时在四个近期SITS数据集上显著提升了农业时间序列无监督分类的现有技术水平。