Separation of small targets in multi-wavelength mixtures based on statistical independence

Rami Mowakeaa; Darren K. Emge

doi:10.1117/12.2305061

8 June 2018 Separation of small targets in multi-wavelength mixtures based on statistical independence

Rami Mowakeaa, Darren K. Emge

Proceedings Volume 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII; 106461H (2018) https://doi.org/10.1117/12.2305061
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

Small target detection is a problem common to a diverse number of ﬁelds such as radar, remote sensing, and infrared imaging. In this paper, we consider the application of feature extraction for detection of small hazardous materials in multiwavelength imaging. Since various materials may exist in the area of study each with varying degrees of reﬂectivity and absortion at diﬀerent wavelengths of light, ﬂexible, data-driven methods are needed for feature extraction of relevant sources. We propose the use of independent component analysis (ICA), a widely-used blind source separation method based on the statistical independence of the underlying sources. We compare 3 diﬀerent prominent ﬂavors of ICA on simulated data in a variety of environments. Then, we apply ICA to 2 multi-wavelength imaging datasets with results that suggest that features extracted are useful.

Citation Download Citation

Rami Mowakeaa and Darren K. Emge "Separation of small targets in multi-wavelength mixtures based on statistical independence", Proc. SPIE 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII, 106461H (8 June 2018); https://doi.org/10.1117/12.2305061

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KEYWORDS

Independent component analysis

Feature extraction

Principal component analysis

Computer simulations

Infrared imaging

Data modeling

Performance modeling

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