A bidirectional gradient prediction based method for hyperspectral data junk bands restoration

Abstract

Hyperspectral images (HSIs) are often contaminated by noise, some spectral bands are highly corrupted that they are usually discarded before processing. To make full use of hyperspectral data, a new bidirectional gradient (BG)-prediction-based HSI junk bands restoration algorithm is proposed. Firstly, according to the field spectral reflectance curves continuity and high spectral resolution instruments, both sides of the junk bands reflectance relative to wavelength gradients can be estimated respectively. Thus, calculate the two estimates of each junk band. Finally, followed by introducing the weighting factor which is inversely proportion to the square of wavelength difference and weighting the two estimates, the results of BG-prediction can be obtained. Experiments are implemented using the HIS collected by airborne visible/infrared imaging spectrometer (AVIRIS). Results indicate that compared with linear prediction, bidirectional gradient prediction can effectively improve the restoration performance, meanwhile the ground classification accuracy of the restored HSIs are improved.