  The mainstream CNN-based remote sensing (RS) image semantic segmentation
approaches typically rely on massive labeled training data. Such a paradigm
struggles with the problem of RS multi-view scene segmentation with limited
labeled views due to the lack of considering 3D information within the scene.
In this paper, we propose ''Implicit Ray-Transformer (IRT)'' based on Implicit
Neural Representation (INR), for RS scene semantic segmentation with sparse
labels (such as 4-6 labels per 100 images). We explore a new way of introducing
multi-view 3D structure priors to the task for accurate and view-consistent
semantic segmentation. The proposed method includes a two-stage learning
process. In the first stage, we optimize a neural field to encode the color and
3D structure of the remote sensing scene based on multi-view images. In the
second stage, we design a Ray Transformer to leverage the relations between the
neural field 3D features and 2D texture features for learning better semantic
representations. Different from previous methods that only consider 3D prior or
2D features, we incorporate additional 2D texture information and 3D prior by
broadcasting CNN features to different point features along the sampled ray. To
verify the effectiveness of the proposed method, we construct a challenging
dataset containing six synthetic sub-datasets collected from the Carla platform
and three real sub-datasets from Google Maps. Experiments show that the
proposed method outperforms the CNN-based methods and the state-of-the-art
INR-based segmentation methods in quantitative and qualitative metrics.
