Effective Data Fusion with Generalized Vegetation Index: Evidence from Land Cover Segmentation in Agriculture
This work addresses land cover segmentation in agriculture for remote sensing applications, representing an incremental improvement with specific gains.
The paper tackled the problem of segmenting agriculture land cover from satellite images by proposing a data-fusion approach using a Generalized Vegetation Index (GVI) and Additive Group Normalization (AGN), which improved IoUs of vegetation-related classes by 0.9-1.3 percent and overall mIoU by 2 percent.
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.