多传感器和光谱图像融合与挖掘:从神经系统到应用

D. Fay, R. Ivey, N. Bomberger, A. Waxman
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引用次数: 4

摘要

我们继续开发了一个基于色彩视觉处理、学习和模式识别的神经模型的多传感器图像融合和交互式挖掘系统。我们在麻省理工学院林肯实验室开创了这项工作,最初用于彩色融合夜视(低光可见光和非冷却热图像),后来扩展到多光谱红外和3D阶梯。我们还开发了一个用于EO, IR, SAR融合和采矿的概念验证系统。在过去的一年里,我们推广了这种方法,并开发了一个用户友好的系统,集成到一个被称为ERDAS Imagine的COTS开发环境中。本文总结了低光可见光/SWIR/MWIR/LWIR夜间图像和IKONOS多光谱高分辨率全色图像的融合和交互挖掘(即目标学习和搜索)方法和所使用的神经网络。此外,我们还演示了如何通过允许在多个场景上进行训练来在扩展的操作条件下启用目标学习和搜索。这已用于使用融合可见光/中波红外/低波红外图像检测沿海水域的小船。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multisensor & spectral image fusion & mining: from neural systems to applications
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 ladder. 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 have summarized 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 had demonstrated how target learning and search can be enabled over extended operating conditions by allowing training over multiple scenes. This has been illustrated for the detection of small boats in coastal waters using fused visible/MWIR/LWIR imagery.
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