Weighted principal component analysis fusion of satellite telemetry data

Eric King; Arthur C. Depoian II; Colleen P. Bailey; Parthasarathy Guturu

doi:10.1117/12.2574183

20 September 2020 Weighted principal component analysis fusion of satellite telemetry data

Eric King, Arthur C. Depoian II, Colleen P. Bailey, Parthasarathy Guturu

Proceedings Volume 11529, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2020; 1152904 (2020) https://doi.org/10.1117/12.2574183
Event: SPIE Remote Sensing, 2020, Online Only

Abstract

Satellites are equipped with an array of diversified sensors, capable of relaying multiple types of optical data about the earth’s surface. The different sensors used can capture varying levels of detail for a particular area of interest. Combining information gathered from sensors, ranging from the infrared to the visible spectrum, can enhance visualization and depth of data. The application of principal component analysis (PCA) to data fusion is traditionally processed by weighted reliability matrix. This paper presents a novel weighted reliability with rejection control PCA based sensor algorithm to improve data fusion quality creating a more robust visualization of the composite information obtained from satellites. The proposed algorithm can be applied using both L₂ and L₁ PCA. Simulation studies validate the proposed controlled weighted fusion method, even under high levels of corruption.

Conference Presentation

Citation Download Citation

Eric King, Arthur C. Depoian II, Colleen P. Bailey, and Parthasarathy Guturu "Weighted principal component analysis fusion of satellite telemetry data", Proc. SPIE 11529, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2020, 1152904 (20 September 2020); https://doi.org/10.1117/12.2574183

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