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Rajat Sahay

at Rochester Institute of Technology

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Proceedings Article | 10 June 2024 Presentation + Paper

On aligning SAM to remote sensing data

Rajat Sahay, Andreas Savakis

Proceedings Volume 13037, 1303703 (2024) https://doi.org/10.1117/12.3014163

KEYWORDS: Image segmentation, Remote sensing, Transformers, Image processing, Deep learning

Read Abstract +

The Segment Anything Model (SAM) has demonstrated exceptional capabilities for object segmentation in various settings. In this work, we focus on the remote sensing domain and examine whether SAM’s performance can be improved for overhead imagery and geospatial data. Our evaluation indicates that directly applying the pretrained SAM model to aerial imagery does not yield satisfactory performance due to the domain gap between natural and aerial images. To bridge this gap, we utilize three parameter-efficient fine-tuning strategies and evaluate SAM’s performance across a set of diverse benchmarks. Our results show that while a vanilla SAM model lacks the intrinsic ability to generate accurate masks for smaller objects often found in overhead imagery, fine-tuning greatly improves performance and produces results comparable to current state-of-the-art techniques.

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