On comparing multiple approaches for classifying vessels in imagery

John G. Warner; Vishal M. Patel

doi:10.1117/12.3020336

7 June 2024 On comparing multiple approaches for classifying vessels in imagery

John G. Warner, Vishal M. Patel

Proceedings Volume 13051, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI; 1305112 (2024) https://doi.org/10.1117/12.3020336
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

Deep learning-based approaches, such as Convolutional Neural Nets (CNNs), have shown high performance in classifying contents of images. CNNs, however, have the notable drawbacks of potentially high computing costs, poor explainability, and wide performance variance if the underlying imagery data deviates from the training baseline. As advanced image processing capabilities are matured, the on-board detection of objects in space-based imagery is increasingly proposed. On-board satellite processing applications, which may be resource-limited, can drive the need for simpler models that reduce the necessary computing burden for edge computing applications. This raises the question of how well classic computer vision techniques can compete with more modern approaches. This paper characterizes and compares the performance of multiple computer vision models for the application of distinguishing maritime vessels from typical clutter in commercial electro-optical (EO) satellite imagery. A Support Vector Machine (SVM) model using manually curated features is compared to multiple DL-based models spanning a range of model sizes, with the goal of determining whether classical approaches can compete favorably with DL when computational resources are taken into consideration. Differences in performance and processing resources are characterized between the approaches. Findings include that the SVM-based model may approach the accuracy of some CNN-based models for classifying images of clouds in satellite EO imagery for smaller DL-based models. However, even the smallest DL-based models, which take about the same computational resources as the SVM-based model, generally out-perform the SVM-based model. This finding may have implications for the operational use of on-board processing techniques for satellite payloads.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

John G. Warner and Vishal M. Patel "On comparing multiple approaches for classifying vessels in imagery", Proc. SPIE 13051, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI, 1305112 (7 June 2024); https://doi.org/10.1117/12.3020336

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