Crop Classification under Varying Cloud Cover with Neural Ordinary Differential Equations
Optical satellite sensors cannot see the Earth's surface through clouds. Despite the periodic revisit cycle, image sequences acquired by Earth observation satellites are therefore irregularly sampled in time. State-of-the-art methods for crop classification (and other time series analysis tasks...
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Main Authors | , , , , |
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Format | Journal Article |
Language | English |
Published |
04.12.2020
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Subjects | |
Online Access | Get full text |
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Summary: | Optical satellite sensors cannot see the Earth's surface through clouds.
Despite the periodic revisit cycle, image sequences acquired by Earth
observation satellites are therefore irregularly sampled in time.
State-of-the-art methods for crop classification (and other time series
analysis tasks) rely on techniques that implicitly assume regular temporal
spacing between observations, such as recurrent neural networks (RNNs). We
propose to use neural ordinary differential equations (NODEs) in combination
with RNNs to classify crop types in irregularly spaced image sequences. The
resulting ODE-RNN models consist of two steps: an update step, where a
recurrent unit assimilates new input data into the model's hidden state; and a
prediction step, in which NODE propagates the hidden state until the next
observation arrives. The prediction step is based on a continuous
representation of the latent dynamics, which has several advantages. At the
conceptual level, it is a more natural way to describe the mechanisms that
govern the phenological cycle. From a practical point of view, it makes it
possible to sample the system state at arbitrary points in time, such that one
can integrate observations whenever they are available, and extrapolate beyond
the last observation. Our experiments show that ODE-RNN indeed improves
classification accuracy over common baselines such as LSTM, GRU, and temporal
convolution. The gains are most prominent in the challenging scenario where
only few observations are available (i.e., frequent cloud cover). Moreover, we
show that the ability to extrapolate translates to better classification
performance early in the season, which is important for forecasting. |
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DOI: | 10.48550/arxiv.2012.02542 |