Separation of small targets in multi-wavelength mixtures based on statistical independence
Links to Fileshttps://www.spiedigitallibrary.org/conference-proceedings-of-spie/10646/106461H/Separation-of-small-targets-in-multi-wavelength-mixtures-based-on/10.1117/12.2305061.full?SSO=1
MetadataShow full item record
Type of Work7 pages
Citation of Original PublicationRami Mowakeaa, Darren K. Emge, Separation of small targets in multi-wavelength mixtures based on statistical independence, Proceedings Volume 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII; 106461H (2018) https://doi.org/10.1117/12.2305061
RightsThis item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.
Subjectsindependent component analysis (ICA)
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.