Consideration of smoothing techniques for hyperspectral remote sensing

Abstract

Spectral smoothing filters are popularly used in a large number of modern hyperspectral remote sensing studies for removing noise from the data. However, most of these studies subjectively apply ad hoc measures to select filter types and their parameters. We argue that this subjectively minded approach is not appropriate for choosing smoothing methods for hyperspectral applications. In our case study, it is proved that smoothing filters can cause undesirable changes to statistical characteristics of the spectral data; thereby, affecting the results of the analyses that are based on statistical class models. If preserving statistical properties of the original hyperspectral data is desired, smoothing filters should then be used, if necessary, after careful consideration of which smoothing techniques will minimize disturbances to the statistical properties of the original data. A comparative t-test is proposed as a method for choosing a smoothing filter suitable for hyperspectral data at hand.