The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XL-1
07 Nov 2014
 | 07 Nov 2014

Pre-processing of Xeva-XS imagery for determining spectral reflectance coefficients in laboratory conditions

P. Walczykowski, A. Orych, M. Kedzierski, and A. Fryskowska

Keywords: Remote sensing, Imagery, Accuracy, Processing, Experiment

Abstract. There are two different methodologies which can be used to acquire imagery from which it would be possible to obtain spectral reflectance characteristics – the first based on images of a scene in which a reference panel had been included, and the second based on precisely selected exposure parameters. This paper is concerned with the first of these two methods based on experiments conducted using a 14bit XEVA XS-1.7.320 infrared sensor. The paper firstly describes the effect of different exposure settings on the accuracy with which we can later determine the spectral reflectance coefficients. The next step when working with such imagery in laboratory conditions is to eliminate the effect of the uneven distribution of illumination. In the paper we present two proposed methods for eliminating the uneven distribution of illumination – an additive method and a quotient method. After that it is essential to stretch the DN values. Once again we investigated two possible methods of doing this – firstly, by stretching the data using only the white reference panel, adjusting the maxDN value of the image of the surface of the reference panel to 95%. The second method additionally adds a second reference point – a black reference panel which reflects 5% of incident radiation. The spectral reflectance coefficients of chosen samples acquired using all of the above mentioned methods are compared with reference data obtained using a spectroradiometer. Establishing the most optimal methodologies will greatly increase the accuracy of obtained spectral response coefficients, which at the same time will increase the accuracy with which, in this case, water pollutants will be identified.