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.
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. This is joint work with Siwei Feng, Yuki Itoh and Marco Duarte and was presented the IEEE International Conference on Image Processing (ICIP), October 27-30, 2014 in Paris, France.
Remote Hyperspectral Observers Group
Department of Electrical and Computer Engineering
University of Massachusetts Amherst