Mowakeaa, RamiEmge, Darren K.2018-09-212018-09-212018-06-08Rami 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/10.1117/12.2305061https://doi.org/10.1117/12.2305061http://hdl.handle.net/11603/11342© SPIE Rami Mowakeaa, 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/10.1117/12.2305061Small 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.7 pagesen-USThis 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.independent component analysis (ICA)machine learningSmall-target detectionBSSmulti-wavelengthText