Abstract
ABSTRACT In this study, we introduce two spatial approaches to enhance semantic segmentation accuracy in remote sensing imagery in resource-constrained computational environments, with a focus on edge regions. The first approach, Contextual Bands Addition, integrates overview, incorporating essential contextual information. The second approach, multi-look inferencing, utilises multiple spatial perspectives to refine segmentation. Our results show significant improvements: Contextual Band Addition with tile overlaps increases IoU scores by 4–5%. Multi-look inferencing enhances IoU scores by 2.5% post-training. Combined, these strategies yield a 6–7% overall performance boost, valuable for semantic segmentation on limited training samples from satellite imagery datasets.
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