Boundary SAM: Improved parcel boundary delineation using SAM's image embeddings and detail enhancement filters

Accurate agricultural parcel boundary delineation is essential in remote sensing applications, yet traditional supervised methods require extensive annotated datasets and often fail to generalize across diverse landscapes. The Segment Anything Model (SAM), a foundational model for zero-shot segmentation, provides scalability but struggles with certain remote sensing challenges, particularly agricultural parcels.In this paper, we propose a novel approach to enhance SAM's performance by leveraging its embeddings to extract meaningful features. Our method applies principal component analysis (PCA) for dimensionality reduction, high-frequency decomposition, and guided filtering to enhance input images, aligning them better with SAM's strengths. By refining the input data through these steps, we improve SAM's ability to delineate parcel boundaries effectively. Experimental results demonstrate consistent improvements across SAM back-bone sizes and parameter settings, achieving higher accuracy in segmentation metrics such as under-segmentation (US) rate, over-segmentation (OS) rate, intersection over union (IoU), and false negative (FN) rate.