Preprocessing of hyperspectral images: a comparative study of destriping algorithms for EO1-hyperion

Abstract

In this study, data from the EO-1 Hyperion instrument were used. Apart from atmospheric influences or topographic effects, the data represent a good choice in order to show different steps of the preprocessing process targeting sensorinternal sources of errors. These include the diffuse sensor noise, the striping effect, the smile effect, the keystone effect and the spatial misalignments between the detector arrays. For this research paper, the authors focus on the striping effect by comparing and evaluating different algorithms, methods and configurations to correct striping errors. The correction of striping effects becomes necessary due to the imprecise calibration of the detector array. This inaccuracy affects especially the first 12 visual and near infrared bands (VNIR) and also a large number of the bands in the short wave infrared array (SWIR). Altogether six destriping techniques were tested on the basis of a Hyperion dataset covering a test site in Central Europe. For the final evaluation, various analyses across all Hyperion channels were performed. The results show that some correction methods have almost no effect on the striping in the images. Other methods may eliminate the striping, but analyses show that these algorithms also alter pixel values in adjacent areas which originally had not been disturbed by the striping effect.

Normalized Difference Vegetation Vigour Index: A New Remote Sensing Approach to Biodiversity Monitoring in Oil Polluted Regions

“…Although these wavelengths showed sensitivity to soil TPH concentrations, Chl-a absorption in the blue range was most affected as the reflectance difference between polluted and control vegetation at that wavelength was up to 300%. In contrast, [98] found that the wavelength around 650 nm was more sensitive to chlorophyll content in vegetation than the chlorophyll absorption features in the blue range.…”

“…Generally, image pre-processing is performed to eliminate noise and other artefacts arising from atmospheric interferences and internal sensor defects [98][99][100]. In this study, the Hyperion image was pre-processed as shown in Table 1.…”

A new land-cover match-based change detection for hyperspectral imagery

“…Wu, Du, & Zhang, 2013). Nowadays, with the development of technologies for airborne systems and space-borne remote-sensing sensors, the ability to obtain data in both high spatial and high spectral resolution is increasingly feasible (Scheffler & Karrasch, 2013). This leads to the use of remote-sensing satellites in various areas of Earth science studies, including object detection Cheng, Zhou, & Han, 2016), image classification (Cheng, Han, Zhou, & Guo, 2014;Cheng et al, 2015;Hassanzadeh & Karami, 2016;Raczko & Zagajewski, 2017), anomaly detection (AD) (L. Zhang & Zhao, 2017) and CD (Shah- Hosseini, Homayouni, & Safari, 2015;K.…”

Chemometric methods applied in the image plane to correct striping noise in hyperspectral chemical images of biomaterials

“…However, it was found that linear array detectors in the IR imaging instrument are prone to striping noise patterns, which are difficult to remove . Scheffler and Karrasch have observed the image stripes in the remote sensing data using a typical pushbroom hyperspectral sensor. They further claimed that uncorrected striping effect could lead to faulty interpretation results of the spectral data, which is particularly applicable to research projects where a high similarity of relevant signatures exists.…”

“…Additionally, they have tested 6 destriping techniques and the results showed that some correction methods have almost no effect on eliminating stripes in the images. Others may remove stripes, yet these algorithms also alter pixel values in adjacent areas, which originally had not been disturbed by the striping effect . Hence, the aim of this research was to investigate the striping noise introduced by array detectors and proposed a new correction method to enhance image quality.…”