Incorporating spatial information in spectral unmixing: A review

Abstract

Spectral unmixing is the process of decomposing the spectral signature of a mixed pixel into a set of endmembers and their corresponding abundances. Endmembers are spectra of the pure materials present in the image and abundances at each pixel represent the percentage of each endmember that is present in the pixel. Many spectral unmixing techniques treat a pixel as independent of its neighbors, therefore, only spectral characteristics of the image are used to address the spectral unmixing problem. However, a number of recent studies have found that spatial autocorrelation provides useful information for spectral unmixing. Combining spatial information with its spectral counterpart can lead to improvements in the unmixing results. In this paper, the unmixing methods that incorporate spatial information are termed spatial spectral unmixing, whereas those exploiting only spectral information are referred to as spectral-only unmixing. We summarize the available spatial spectral unmixing methods according to the following three categories: 1) endmember extraction, 2) selection of endmember combinations, and 3) abundance estimation. An experiment-based comparison between representative spatial spectral and spectral-only unmixing methods is also presented in order to demonstrate the advantages of spatial spectral methods. Furthermore, considerations and suggestions of the incorporation of spatial information are provided. With this review, we hope to bring spatial spectral unmixing to the attention of the remote sensing community and stimulate new research initiatives to integrate both spatial and spectral information for unmixing purposes.