Self-guided Few-shot Semantic Segmentation for Remote Sensing Imagery Based on Large Vision Models

Xiyu Qi; Yifan Wu; Yongqiang Mao; Wenhui Zhang; Yidan Zhang

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arXiv · 2311.13200

Self-guided Few-shot Semantic Segmentation for Remote Sensing Imagery Based on Large Vision Models

Xiyu Qi·Yifan Wu·Yongqiang Mao·Wenhui Zhang·Yidan Zhang

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Abstract

The Segment Anything Model (SAM) exhibits remarkable versatility and zero-shot learning abilities, owing largely to its extensive training data (SA-1B). Recognizing SAM's dependency on manual guidance given its category-agnostic nature, we identified unexplored potential within few-shot semantic segmentation tasks for remote sensing imagery. This research introduces a structured framework designed for the automation of few-shot semantic segmentation. It utilizes the SAM model and facilitates a more efficient generation of semantically discernible segmentation outcomes. Central to our methodology is a novel automatic prompt learning approach, leveraging prior guided masks to produce coarse pixel-wise prompts for SAM. Extensive experiments on the DLRSD datasets underline the superiority of our approach, outperforming other available few-shot methodologies.

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Xiyu Qi, Yifan Wu, Yongqiang Mao, Wenhui Zhang, Yidan Zhang. 2023-11-22. Self-guided Few-shot Semantic Segmentation for Remote Sensing Imagery Based on Large Vision Models. https://arxiv.org/abs/2311.13200

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