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10 June 2024 On aligning SAM to remote sensing data
Rajat Sahay, Andreas Savakis
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Proceedings Volume 13037, Geospatial Informatics XIV; 1303703 (2024) https://doi.org/10.1117/12.3014163
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States
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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(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Rajat Sahay and Andreas Savakis "On aligning SAM to remote sensing data", Proc. SPIE 13037, Geospatial Informatics XIV, 1303703 (10 June 2024); https://doi.org/10.1117/12.3014163
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KEYWORDS
Image segmentation
Remote sensing
Transformers
Image processing
Deep learning
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