Satellite Image Time Series (SITS) of the Earth's surface provide detailed land cover maps, with their quality in the spatial and temporal dimensions consistently improving. These image time series are integral for developing systems that aim to produce accurate, up-to-date land cover maps of the Earth's surface. Applications are wide-ranging, with notable examples including ecosystem mapping, vegetation process monitoring and anthropogenic land-use change tracking. Recently proposed methods for SITS classification have demonstrated respectable merit, but these methods tend to lack native mechanisms that exploit the temporal dimension of the data; commonly resulting in extensive data pre-processing contributing to prohibitively long training times. To overcome these shortcomings, Temporal CNNs have recently been employed for SITS classification tasks with encouraging results. This paper seeks to survey this method against a plethora of other contemporary methods for SITS classification to validate the existing findings in recent literature. Comprehensive experiments are carried out on two benchmark SITS datasets with the results demonstrating that Temporal CNNs display a superior performance to the comparative benchmark algorithms across both studied datasets, achieving accuracies of 95.0\% and 87.3\% respectively. Investigations into the Temporal CNN architecture also highlighted the non-trivial task of optimising the model for a new dataset.

翻译：地球表面的卫星图像时间序列（SITS）可提供详细的土地覆盖图，其在空间和时间维度上的质量持续提升。这类图像时间序列对于开发旨在生成地球表面精确、最新土地覆盖图的系统至关重要。其应用范围广泛，显著实例包括生态系统制图、植被过程监测以及人为土地利用变化跟踪。近年来提出的SITS分类方法已展现出可观的成效，但这些方法往往缺乏利用数据时间维度的内在机制，通常导致大量数据预处理，从而造成训练时间过长。为克服这些不足，时间卷积神经网络（Temporal CNN）近期被应用于SITS分类任务，并取得了令人鼓舞的结果。本文旨在将这一方法与众多其他当代SITS分类方法进行对比调查，以验证近期文献中的现有发现。在两个基准SITS数据集上进行了全面实验，结果表明，时间CNN在两个研究数据集上均表现出优于对比基准算法的性能，分别达到了95.0%和87.3%的准确率。对时间CNN架构的研究也突显了为新数据集优化模型这一非平凡任务。