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An Enhanced Wavelet Expectation-Maximization algorithm for Hyperspectral Image Segmentation

Shaheera Rashwan, Hanan A. Hassan, Noha Shawky


Hyperspectral imaging (HSI) captures a densely sampled spectral response of a scene object over a broad spectrum including invisible spectra such as ultra-violet (UV) and near-infrared(NIR). In this paper, we propose a new Expectation-Maximization (EM) classification approach for
hyperspectral images. The new approach is based on Haar discrete wavelet and histogram equalization. The performance of the proposed method was assessed by carrying out experiments on the AVIRIS dataset. The results show a significant improvement in classification accuracy and time when compared with the results obtained by the conventional EM algorithm.


Hyperspectral imaging, Expectation-Maximization algorithm, Haar discrete wavelet, Histogram equalization, Image Fusion.

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