15 October 2015 HMM for hyperspectral spectrum representation and classification with endmember entropy vectors
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Abstract
The Hyperspectral images due to its good spectral resolution are extensively used for classification, but its high number of bands requires a higher bandwidth in the transmission data, a higher data storage capability and a higher computational capability in processing systems. This work presents a new methodology for hyperspectral data classification that can work with a reduced number of spectral bands and achieve good results, comparable with processing methods that require all hyperspectral bands. The proposed method for hyperspectral spectra classification is based on the Hidden Markov Model (HMM) associated to each Endmember (EM) of a scene and the conditional probabilities of each EM belongs to each other EM. The EM conditional probability is transformed in EM vector entropy and those vectors are used as reference vectors for the classes in the scene. The conditional probability of a spectrum that will be classified is also transformed in a spectrum entropy vector, which is classified in a given class by the minimum ED (Euclidian Distance) among it and the EM entropy vectors. The methodology was tested with good results using AVIRIS spectra of a scene with 13 EM considering the full 209 bands and the reduced spectral bands of 128, 64 and 32. For the test area its show that can be used only 32 spectral bands instead of the original 209 bands, without significant loss in the classification process.
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Samir Youssif Wehbi Arabi, Samir Youssif Wehbi Arabi, David Fernandes, David Fernandes, Marco Antonio Pizarro, Marco Antonio Pizarro, } "HMM for hyperspectral spectrum representation and classification with endmember entropy vectors", Proc. SPIE 9643, Image and Signal Processing for Remote Sensing XXI, 96430P (15 October 2015); doi: 10.1117/12.2194135; https://doi.org/10.1117/12.2194135
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