Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 295-297, 2016
https://doi.org/10.5194/isprs-archives-XLI-B7-295-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
21 Jun 2016
MULTIPLE REFLECTION EFFECTS IN NONLINEAR MIXTURE MODEL FOR HYPERSPECTRAL IMAGE ANALYSIS
C. Y. Liu1 and H. Ren2 1Dept. of Computer and Information Engineering, National Central University, Taiwan
2Center for Space and Remote Sensing Research, National Central University, Taiwan
Keywords: Hyperspectral images, Linear mixture model (LMM), Generalized bilinear model (GBM), Modified generalized bilinear model (MGBM) Abstract. Hyperspectral spectrometers can record electromagnetic energy with hundreds or thousands of spectral channels. With such high spectral resolution, the spectral information has better capability for material identification. Because of the spatial resolution, one pixel in hyperspectral images usually covers several meters, and it may contain more than one material. Therefore, the mixture model must be considered. Linear mixture model (LMM) has been widely used for remote sensing target classifications, because of its simplicity and yields reasonable results for smooth surfaces. For rough surfaces, the physical interactions of the light scattered between multiple materials in the scene must be considered. Recently, Generalized Bilinear Model (GBM) is proposed and it includes the double reflection between different materials into a nonlinear model, but it ignores the interactions within the same material. In this study, we propose a modified version of GBM to further consider this effect in our model, called Modified Generalized Bilinear Model (MGBM).
Conference paper (PDF, 1315 KB)


Citation: Liu, C. Y. and Ren, H.: MULTIPLE REFLECTION EFFECTS IN NONLINEAR MIXTURE MODEL FOR HYPERSPECTRAL IMAGE ANALYSIS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 295-297, https://doi.org/10.5194/isprs-archives-XLI-B7-295-2016, 2016.

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