Abstract
Traditional supervised target detection methods need target spectra as prior knowledge. When the target spectra can only be acquired from the lab or field, they may be very different from the real target spectra obtained from images, which results in low accuracy of these target detection methods. In order to solve this problem, a new preprocessing method used for target detection in hyperspectral imagery is proposed. This preprocessing method can raise target spectra accuracy, so the performance of the target detection methods can be improved. By using the target spectra gotten from the lab as references, the proposed method extracts independent components, which are the closest to the references, from the hyperspectral imagery by means of independent component analysis with references (ICA-R). Then, these independent components are used as target spectra in the following supervised target detection methods. Experimental results on both simulated and real hyperspectral data demonstrate that the proposed method can get more accurate target spectra, which obtains much better performance of target detection.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call