SegDesicNet: Lightweight Semantic Segmentation in Remote Sensing with Geo-Coordinate Embeddings for Domain Adaptation

Sachin Verma, Frank Lindseth, Gabriel Kiss

Abstract:

Semantic segmentation is essential for analyzing highdefinition remote sensing images (HRSIs) because it allows
the precise classification of objects and regions at the pixel
level. However, remote sensing data present challenges
owing to geographical location, weather, and environmental variations, making it difficult for semantic segmentation models to generalize across diverse scenarios. Existing methods are often limited to specific data domains
and require expert annotators and specialized equipment
for semantic labeling. In this study, we propose a novel
unsupervised domain adaptation technique for remote sensing semantic segmentation by utilizing geographical coordinates that are readily accessible in remote sensing setups as metadata in a dataset. To bridge the domain gap,
we propose a novel approach that considers the combination of an image’s location-encoding trait and the spherical nature of Earth’s surface. Our proposed SegDesicNet
module regresses the GRID positional encoding of the geocoordinates projected over the unit sphere to obtain the
domain loss. Our experimental results demonstrate that
the proposed SegDesicNet outperforms state-of-the-art domain adaptation methods in remote sensing image segmentation, achieving an improvement of approximately 6% in
the mean intersection over union (MIoU) with a ∼ 27%
drop in parameter count on benchmarked subsets of the
publicly available FLAIR #1 dataset. We also benchmarked
our method performance on the custom split of the ISPRS
Potsdam dataset. Our algorithm seeks to reduce the modeling disparity between artificial neural networks and human
comprehension of the physical world, making the technology more human-centric and scalable