Automatic Detection and Removal of Spiked Points in Hyperspectral Images

Abstract

This paper presents an approach to eliminate one of the most common defects in hyperspectral images—the appearance of spiked points at some wavelengths. The elimination of this defect was carried out by means of polynomial regression. The Bayes Information Criterion (BIC) was used to determine the correct order of the polynomial. Comparison between polynomial regression and classical filtration with the Savitsky–Golay method shows the advantage of the proposed approach, from the point of view of eliminating the defect in a local area, without changing the typical behavior of the spectral feature in the affected image pixels.

Keywords

• hyperspectral images
• preprocessing and filtering
• defect pixels
• automatic detection