17 May 2016 Supervised target detection in hyperspectral images using one-class Fukunaga-Koontz Transform
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Abstract
A novel hyperspectral target detection technique based on Fukunaga-Koontz transform (FKT) is presented. FKT offers significant properties for feature selection and ordering. However, it can only be used to solve multi-pattern classification problems. Target detection may be considered as a two-class classification problem, i.e., target versus background clutter. Nevertheless, background clutter typically contains different types of materials. That’s why; target detection techniques are different than classification methods by way of modeling clutter. To avoid the modeling of the background clutter, we have improved one-class FKT (OC-FKT) for target detection. The statistical properties of target training samples are used to define tunnel-like boundary of the target class. Non-target samples are then created synthetically as to be outside of the boundary. Thus, only limited target samples become adequate for training of FKT. The hyperspectral image experiments confirm that the proposed OC-FKT technique provides an effective means for target detection.
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Hamidullah Binol, Hamidullah Binol, Abdullah Bal, Abdullah Bal, "Supervised target detection in hyperspectral images using one-class Fukunaga-Koontz Transform", Proc. SPIE 9842, Signal Processing, Sensor/Information Fusion, and Target Recognition XXV, 98421G (17 May 2016); doi: 10.1117/12.2223917; https://doi.org/10.1117/12.2223917
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