The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XL-1/W5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W5, 475–478, 2015
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W5, 475–478, 2015

  11 Dec 2015

11 Dec 2015


M. R. Mobasheri, S. Dehnavi, and Y. Maghsoudi M. R. Mobasheri et al.
  • Geodesy and Geomatics Engineering Faculty, K.N.Toosi University of Technology, Tehran, Iran

Keywords: Linear mixing, spectral correction, laboratory measurements, hyperspectral imaging

Abstract. In order to understand the characteristics of the data collected by hyperspectral imaging systems, it is important to discuss the physics behind the scene radiance field incident on the imaging system. A dominant effect in hyperspectral remote sensing is the mixing of radiant energies contributed from different materials present in a given pixel. The basic assumption of mixture modelling is that within a given scene, the surface is covered by a small number of distinct materials that have relatively constant spectral properties. It is most common to assume that the radiance reflected by different materials in a pixel can spectrally combine in a linear additive manner to produce the pixel radiance/reflectance, even when that might not be the case e.g. where the mixing process leads to nonlinear combinations of the radiance and where the linear assumption fails to hold. This can occur where there is significant relative three-dimensional structure within a given pixel. Without detailed knowledge of the dimensional structure, it can be very difficult to correctly ‘‘un-mix’’ the contributions of the various materials. This work aims to evaluate the correctness of the linear assumption in the mixture modelling using some laboratory measurements. Study was conducted using some sheets made of cellulose materials of different colours in 400-800 nm spectral range. Experimental results have shown that a correction term must be applied to the gains and offsets in the linear model. The obtained results can be extended to satellite sensors that acquire images in the above mentioned spectral range.