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30 May 2022 Methods of fused EO/SAR deep learning
Erik Blasch, Andreas Savakis
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Proceedings Volume PC12096, Automatic Target Recognition XXXII; PC1209604 (2022) https://doi.org/10.1117/12.2622253
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States
Abstract
Deep learning methods have exploded to enhance imagery-based target recognition, scene observation, and context analysis. Image fusion methods consist of many applications, especially when the image modalities are collected simultaneously such as with electro-optical and infrared imagers. When the modalities are collected from different platforms, methods of image fusion require more care for image registration, but with the advances in deep learning; data analysis can minimize the impact of varying operating conditions (e.g., sensor, environment, target). One example or importance is that of fusing electro-optical (EO) and synthetic aperture radar (SAR). This paper reviews methods in EO/SAR fusion and assesses the current methods of EO/SAR in image fusion, machine learning, and deep learning. Prior work in EO/SAR imagery had limited data collections, but machine learning was applied.
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Erik Blasch and Andreas Savakis "Methods of fused EO/SAR deep learning", Proc. SPIE PC12096, Automatic Target Recognition XXXII, PC1209604 (30 May 2022); https://doi.org/10.1117/12.2622253
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KEYWORDS
Image fusion
Electro optics
Machine learning
Sensors
Synthetic aperture radar
Imaging systems
Infrared imaging
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