Earth observation (EO) sensors deliver data with daily or weekly temporal
resolution. Most land use and land cover (LULC) approaches, however, expect
cloud-free and mono-temporal observations. The increasing temporal capabilities
of today's sensors enables the use of temporal, along with spectral and spatial
features. Domains, such as speech recognition or neural machine translation,
work with inherently temporal data and, today, achieve impressive results using
sequential encoder-decoder structures. Inspired by these sequence-to-sequence
models, we adapt an encoder structure with convolutional recurrent layers in
order to approximate a phenological model for vegetation classes based on a
temporal sequence of Sentinel 2 (S2) images. In our experiments, we visualize
internal activations over a sequence of cloudy and non-cloudy images and find
several recurrent cells, which reduce the input activity for cloudy
observations. Hence, we assume that our network has learned cloud-filtering
schemes solely from input data, which could alleviate the need for tedious
cloud-filtering as a preprocessing step for many EO approaches. Moreover, using
unfiltered temporal series of top-of-atmosphere (TOA) reflectance data, we
achieved in our experiments state-of-the-art classification accuracies on a
large number of crop classes with minimal preprocessing compared to other
classification approaches.

本研究提出一种基于序列编码器 - 解码器结构的植被类的物候模型，特别考虑了观测序列中的云量过滤，最终使用无需繁琐的预处理步骤实现了无云涵盖下对不同种类的农作物进行地表覆盖分类的最佳结果。