  How can we effectively leverage the domain knowledge from remote sensing to
better segment agriculture land cover from satellite images? In this paper, we
propose a novel, model-agnostic, data-fusion approach for vegetation-related
computer vision tasks. Motivated by the various Vegetation Indices (VIs), which
are introduced by domain experts, we systematically reviewed the VIs that are
widely used in remote sensing and their feasibility to be incorporated in deep
neural networks. To fully leverage the Near-Infrared channel, the traditional
Red-Green-Blue channels, and Vegetation Index or its variants, we propose a
Generalized Vegetation Index (GVI), a lightweight module that can be easily
plugged into many neural network architectures to serve as an additional
information input. To smoothly train models with our GVI, we developed an
Additive Group Normalization (AGN) module that does not require extra
parameters of the prescribed neural networks. Our approach has improved the
IoUs of vegetation-related classes by 0.9-1.3 percent and consistently improves
the overall mIoU by 2 percent on our baseline.
