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11 August 2004 Multisensor image fusion and mining: learning targets across extended operating conditions
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Abstract
We have continued development of a system for multisensor image fusion and interactive mining based on neural models of color vision processing, learning and pattern recognition. We pioneered this work while at MIT Lincoln Laboratory, initially for color fused night vision (low-light visible and uncooled thermal imagery) and later extended it to multispectral IR and 3D ladar. We also developed a proof-of-concept system for EO, IR, SAR fusion and mining. Over the last year we have generalized this approach and developed a user-friendly system integrated into a COTS exploitation environment known as ERDAS Imagine. In this paper, we will summarize the approach and the neural networks used, and demonstrate fusion and interactive mining (i.e., target learning and search) of low-light Visible/SWIR/MWIR/LWIR night imagery, and IKONOS multispectral and high-resolution panchromatic imagery. In addition, we will demonstrate how target learning and search can be enabled over extended operating conditions by allowing training over multiple scenes. This will be illustrated for the detection of small boats in coastal waters using fused Visible/MWIR/LWIR imagery.
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David A. Fay, Allen M. Waxman, Richard Ivey, Neil A. Bomberger, and Marianne Chiarella "Multisensor image fusion and mining: learning targets across extended operating conditions", Proc. SPIE 5424, Enhanced and Synthetic Vision 2004, (11 August 2004); https://doi.org/10.1117/12.554496
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