Target detection from noise-reduced hyperspectral imagery using a spectral unmixing approach

Shen-En Qian; Josée Lévesque

doi:10.1117/1.3077179

1 February 2009 Target detection from noise-reduced hyperspectral imagery using a spectral unmixing approach

Shen-En Qian, Josée Lévesque

Author Affiliations +

Optical Engineering, Vol. 48, Issue 2, 026401 (February 2009). https://doi.org/10.1117/1.3077179

Abstract

We assess the effectiveness of a previously proposed noise reduction technology for hyperspectral imagery to examine whether it can better serve remote sensing applications after noise reduction using the technology. Target detection from hyperspectral imagery using a spectral unmixing approach is selected as an example in the assessment. A hyperspectral datacube acquired using an airborne short-wave-infrared Full Spectrum Image II with man-made targets in the scene of the datacube is tested. Three criteria are proposed and used to evaluate the detectability of the targets derived from the datacube before and after noise reduction. The evaluation results show that the detectability of the targets is significantly improved after noise reduction using the technology. The targets not detected from the original datacube are detected with high confidence after noise reduction using the technology. A noise reduction technique that is based on a smoothing approach is also evaluated for the sake of comparison to the proposed noise reduction technology. It also improves the detectability of the targets, but is less effective than the proposed noise reduction technology.

©(2009) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Shen-En Qian and Josée Lévesque "Target detection from noise-reduced hyperspectral imagery using a spectral unmixing approach," Optical Engineering 48(2), 026401 (1 February 2009). https://doi.org/10.1117/1.3077179

Published: 1 February 2009

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