Chapter

Optical Remote Sensing

Volume 3 of the series Augmented Vision and Reality pp 235-267

Date:

Recent Developments in Endmember Extraction and Spectral Unmixing

  • Antonio PlazaAffiliated withDepartment of Technology of Computers and Communications, University of Extremadura Email author 
  • , Gabriel MartínAffiliated withDepartment of Technology of Computers and Communications, University of Extremadura
  • , Javier PlazaAffiliated withDepartment of Technology of Computers and Communications, University of Extremadura
  • , Maciel ZorteaAffiliated withDepartment of Mathematics and Statistics, University of Tromso
  • , Sergio SánchezAffiliated withDepartment of Technology of Computers and Communications, University of Extremadura

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Abstract

Spectral unmixing is an important task for remotely sensed hyperspectral data exploitation. The spectral signatures collected in natural environments are invariably a mixture of the pure signatures of the various materials found within the spatial extent of the ground instantaneous field view of the imaging instrument. Spectral unmixing aims at inferring such pure spectral signatures, called endmembers, and the material fractions, called fractional abundances, at each pixel of the scene. In this chapter, we provide an overview of existing techniques for spectral unmixing and endmember extraction, with particular attention paid to recent advances in the field such as the incorporation of spatial information into the endmember searching process, or the use of nonlinear mixture models for fractional abundance characterization. In order to substantiate the methods presented throughout the chapter, highly representative hyperspectral scenes obtained by different imaging spectrometers are used to provide a quantitative and comparative algorithm assessment. To address the computational requirements introduced by hyperspectral imaging algorithms, the chapter also includes a parallel processing example in which the performance of a spectral unmixing chain (made up of spatial–spectral endmember extraction followed by linear spectral unmixing) is accelerated by taking advantage of a low-cost commodity graphics co-processor (GPU). Combined, these parts are intended to provide a snapshot of recent developments in endmember extraction and spectral unmixing, and also to offer a thoughtful perspective on future potentials and emerging challenges in designing and implementing efficient hyperspectral imaging algorithms.

Keywords

Hyperspectral imaging Spectral unmixing Endmember extraction Neural networks Intelligent training Parallel processing GPUs