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RemoteSensingCorpus

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FIGURE 6. Another approach to hybrid ML modeling is that of
including layers with physics motivation, which are learned from
data end to end, into a deep neural network. The architecture
learns a motion field with a convolution–deconvolution network,
and the motion field is further processed using a warping physical
model. The error is used to adjust the network weights, and, after
training, the model can produce multiple time-step predictions
recursively. (Adapted from [70].)
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JUNE 2021 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINEand its components to understand, for example, why
the model came to a certain decision. Therefore, expla -
nations become application dependent, and identical
interpretations can lead to different explanations when
linked to different domain knowledge.
HOW EXPLAINABLE AI MAY HELP REMOTE SENSING
Recently, many tools have been proposed for increasing
interpretability and explainability when combined with
domain knowledge [ 18]. Two major groups have emerged.
1) Posthoc interpretability : In this group, the outcomes and
decisions of the model are interpreted and explained
by looking at the input. The most common visualiza -
tions for interpretations are heatmaps and prototypes.
Heatmaps highlight the parts of the input data that are
prominent, important, or occlusion sensitive; for ex -
ample, they are created using the gradient flows in the
neural network. Prototypes are optimized input data
that, given a model, maximize the targeted output. Both
of these approaches help to explain what a model bases
its decisions on, what influences the output, or what is a
typical input for the learned input–output relationships.
In all cases, attention must be paid to the confirmation
bias, which is defined as the tendency to try to explain interpretations that are consistent with our existing
knowledge even if the explanation does not apply to the
given case (see [ 74] for an example of the overinterpreta -
tion of saliency maps).
2) Interpretability by design : In this case, the model is inher -
ently designed so that it can be interpreted. Interpret -
ability is achieved by representing model components
or obtained latent variables such that they can be ex -
plained with knowledge from a certain application
domain. For instance, the units in hidden layers can
be designed in such a way that the underlying factors
of variation, such as the driving forces in Earth system
data, become disentangled and are captured in separate
units. This could be seen, for example, by the fact that
simple correlations exist between variations of the input
and the activation of the neurons. Interesting applica -
tions of this idea are proposed in [ 75], where the authors
disentangle the physical forces applied between objects
in videos, or in [ 76], for explaining human perceptions
of beauty in landscapes.
To ensure the scientific value of the output, interpreta -
tion tools can be used to check its reliability. Besides the
inherently existing output score of the neural network,
for example, visualizations of the processes within the
Explainability
Interpretability
TransparencyScientific
Discoveries
and Insights
Model OutputML Model
Earth DataDomain Knowledge
(Reference Labels, Exper t
Knowledge, and so on)
FIGURE 7. Explainable ML can be used to gain scientific discoveries and insights by explaining a learned model and/or results (shown in
the light gray box). The prerequisites are interpretability and potential transparency, which lead to scientific explanations when combined
with domain knowledge. A feedback loop allows for extending and improving the known domain knowledge. One potential application
is the derivation of improved definitions, for example, for certain land-use classes, which are currently only vaguely, incompletely, or not
uniformly described.
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98
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE JUNE 2021neural network can be used to check whether correct deci -
sions have been made for the wrong reasons (the so-called
Clever Hans effect [ 78]). This can be seen as an additional
test for the reliability of the output due to the fact that a
high score for the network does not always mean a correct
result. In summary, these tools can increase confidence
by improving traceability, as estimates are generated and
reveal biases in the data through human-understand -
able visualization.
PERSPECTIVES
Explainable ML has thus far received comparatively little
attention in remote sensing, partly because of the still-
predominant opinion that explainability is tightly coupled
with the complexity of a model and, therefore, an increase
in explainability leads directly to a decrease in accuracy
(e.g., in [ 79]). In the meantime, however, several applica -
tions have shown that this is no longer the case.
Most of the approaches considered thus far are post hoc
interpretations, but initial approaches that consider inter -
pretability by design are appearing. In [ 76], for instance, the
model is forced to predict human-interpretable concepts
before predicting the final task ( Figure 8 ). Such approaches
have the potential to provide both reliability checks and human-understandable explanations and could be used to
move toward physics-explicit models, similar to those dis -
cussed in the next section.
As a further step, explanations going beyond today’s