Investigation into Few-Shot Instance Segmentation of Agricultural Landscapes Utilizing SAM-Driven Model

Abstract

In complex agricultural landscapes, especially with limited sample sizes, current instance segmentation techniques often grapple with defining clear boundaries, thereby compromising segmentation accuracy. To mitigate this challenge, our research proposes enhancements through quantitative comparisons and the incorporation of the SAM model to bolster instance segmentation networks. Specifically, we have formulated SAM+Mask2Former and SAM+Mask R-CNN by adopting SAM's feature encoder as the foundational backbone. Further refinement is accomplished by integrating the Efficient Channel Attention (ECA) module and optimizing prompts, culminating in the SAM+ECA+Prompter model. Impressively, these models exhibit robust generalization capabilities when applied to high-resolution remote sensing imagery, even when based on smaller datasets. Empirical evidence demonstrates that, even with a mere 50 samples, the models achieve an Average Precision (AP) and Average Recall (AR) of 0.715. When scaled to 200 samples, AP surges to 0.875, while AR surpasses 0.903—figures that stand toe-to-toe with traditional field segmentation paradigms. Consequently, our research significantly elevates the efficacy of segmentation models, emphasizing a reduction in both labeling and computational expenses, thereby presenting a resource-efficient, high-precision solution for precision agriculture.