Presentation
Tailoring Non-Homogeneous Markov Chain Models for Hyperspectral Signature Classification
IEEE International Conference on Image Processing (ICIP)
(2014)
Abstract
We consider the application of non-homogeneous hidden Markov chain (NHMC) models to the problem of hyperspectral signature classification. It has been previously shown that the NHMC model enables the detection of several semantic structural features of hyperspectral signatures. However, there are some aspects of the spectral data that are not fully captured by the proposed NHMC models such as the relatively smooth but fluctuating regions and the fluctuation orientations. In order to address these limitations, we propose an improved NHMC model based on Daubechies-1 wavelets in conjunction with an increased the model complexity. Experimental results show that the revised approach outperforms existing approaches relevant in classification tasks.
Disciplines
Publication Date
2014
Comments
This is an author manuscript of this conference paper. More information about the conference can be found at http://www.icip2015.org/
Citation Information
Siwei Feng, Yuki Itoh, Mario Parente and Marco Duarte. "Tailoring Non-Homogeneous Markov Chain Models for Hyperspectral Signature Classification" IEEE International Conference on Image Processing (ICIP) (2014) Available at: http://works.bepress.com/marco_duarte/10/