Multisensor Data Fusion with Singular Value Decomposition

S. Koduri
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引用次数: 2

Abstract

The present study aims at multi-sensor data fusion with Singular Value Decomposition (SVD). Earth observations imaging systems collect data at different spatial and radiometric resolutions due to transmission bandwidth and other technical constraints. Fusion of multi-sensor images enables a synergy of complementary information obtained by sensors of different spectral ranges. The study illustrates the excellent potential of Singular Value Decomposition for image fusion with Quick bird panchromatic and multi spectral data. The study brings out that this fusion process outscores conventional techniques used in operational environments and is illustrated with a second example by merging IRS1C panchromatic data with IRSP6 multi spectral data.
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基于奇异值分解的多传感器数据融合
本研究主要针对多传感器数据的奇异值分解(SVD)融合。由于传输带宽和其他技术限制,地球观测成像系统以不同的空间和辐射分辨率收集数据。多传感器图像的融合使不同光谱范围的传感器获得的互补信息协同作用。该研究说明了奇异值分解在快速鸟全色和多光谱图像融合中的良好潜力。该研究表明,该融合过程优于作战环境中使用的传统技术,并通过将IRS1C全色数据与IRSP6多光谱数据合并的第二个例子进行了说明。
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