Translator Disclaimer
18 October 2016 Accuracy assessment of blind and semi-blind restoration methods for hyperspectral images
Author Affiliations +
Hyperspectral images acquired by remote sensing systems are generally degraded by noise and can be sometimes more severely degraded by blur. When no knowledge is available about the degradations present or the original image, blind restoration methods must be considered. Otherwise, when a partial information is needed, semi-blind restoration methods can be considered. Numerous semi-blind and quite advanced methods are available in the literature. So to get better insights and feedback on the applicability and potential efficiency of a representative set of four semi-blind methods recently proposed, we have performed a comparative study of these methods in objective terms of blur filter and original image error estimation accuracy. In particular, we have paid special attention to the accurate recovering in the spectral dimension of original spectral signatures. We have analyzed peculiarities and factors restricting the applicability of these methods. Our tests are performed on a synthetic hyperspectral image, degraded with various synthetic blurs (out-of-focus, gaussian, motion) and with signal independent noise of typical levels such as those encountered in real hyperspectral images. This synthetic image has been built from various samples from classified areas of a real-life hyperspectral image, in order to benefit from realistic reference spectral signatures to recover after synthetic degradation. Conclusions, practical recommendations and perspectives are drawn from the results experimentally obtained.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mo Zhang, Benoit Vozel, Kacem Chehdi, Mykhail Uss, Sergey Abramov, and Vladimir Lukin "Accuracy assessment of blind and semi-blind restoration methods for hyperspectral images", Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 100040P (18 October 2016);

Back to Top