论文标题
调查卫星图像时间序列分类的时间卷积神经网络：调查
Investigating Temporal Convolutional Neural Networks for Satellite Image Time Series Classification: A survey
论文作者
论文摘要
地球表面的卫星图像时间序列（坐着）提供了详细的土地覆盖图，其质量在空间和时间尺寸始终如一地改善。这些图像时间序列对于开发旨在生成地球表面的准确，最新的土地覆盖地图的系统而言是不可或缺的。应用是广泛的，其中包括生态系统映射，植被过程监测和人为土地利用变化跟踪。最近提出的用于分类的方法表现出了可观的优点，但是这些方法往往缺乏利用数据时间维度的天然机制。通常导致广泛的数据预处理，导致过长的训练时间。为了克服这些缺点，暂时的CNN最近被用于分类任务，结果令人鼓舞。本文旨在针对其他当代方法进行分类以验证最近文献中现有发现的其他当代方法。全面的实验是在两个基准坐标的数据集上进行的，结果表明，时间CNNS在两个研究的数据集中表现出优于比较基准测试算法的性能，分别达到95.0 \％和87.3 \％的准确性。对时间CNN体系结构的调查还强调了优化新数据集模型的非平凡任务。
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.
