  Spectral-spatial based deep learning models have recently proven to be
effective in hyperspectral image (HSI) classification for various earth
monitoring applications such as land cover classification and agricultural
monitoring. However, due to the nature of "black-box" model representation, how
to explain and interpret the learning process and the model decision,
especially for vegetation classification, remains an open challenge. This study
proposes a novel interpretable deep learning model -- a biologically
interpretable two-stage deep neural network (BIT-DNN), by incorporating the
prior-knowledge (i.e. biophysical and biochemical attributes and their
hierarchical structures of target entities) based spectral-spatial feature
transformation into the proposed framework, capable of achieving both high
accuracy and interpretability on HSI based classification tasks. The proposed
model introduces a two-stage feature learning process: in the first stage, an
enhanced interpretable feature block extracts the low-level spectral features
associated with the biophysical and biochemical attributes of target entities;
and in the second stage, an interpretable capsule block extracts and
encapsulates the high-level joint spectral-spatial features representing the
hierarchical structure of biophysical and biochemical attributes of these
target entities, which provides the model an improved performance on
classification and intrinsic interpretability with reduced computational
complexity. We have tested and evaluated the model using four real HSI datasets
for four separate tasks (i.e. plant species classification, land cover
classification, urban scene recognition, and crop disease recognition tasks).
The proposed model has been compared with five state-of-the-art deep learning
models.
