Abstract
Hyperspectral imagery is a main tool of remote sensing applications. As a signal is transmitted towards a given scene, reflected and scattered again by interacting with the various components of the atmosphere and the surface, the reflectance spectra analysis allows recognition and/or quantification of the materials. Hyperspectral image is three-dimensional data cube that containing the values of the radiation that has been collected over an area in a wide range of wavelengths. This hyperspectral data serves to identify the scene composition, and includes applications such as chemical analysis, plant and mineral recognition, and urban mapping. Each reflected signal to form the hyperspectral data can be a mixture of different materials (endmembers). Spectral unmixing is decomposing the hyperspectral image into pure spectral signatures of the materials in the scene, and proportioning every material at pixel location. Despite the recent advances in hyperspectral technology, spectral unmixing, which consists in finding a set of spectrally pure components (endmembers) and their associated fractions coverage for each pixel (abundances) in hyperspectral data, remains a challenging research field. Also, most the state-of-the-art approaches assume that the number of endmembers is known a priori. Some approaches have been proposed to estimate the number of endmembers. These approaches are first applied to the hyperspectral data in order to learn the number of endmembers. Then, the unmixing is performed given the learned parameter. In this paper, we review the number of endmembers estimation techniques used for hyperspectral data unmixing.