Spectral unmixing of urban Landsat imagery by means of neural networks

Mapping urban surfaces using Earth Observation data is one the most challenging tasks of remote sensing field, because of the high spatial and spectral diversity of man-made structures. Spectral unmixing techniques, although designed and mainly used with hyperspectral data, can be proven useful for use with spectral data as well to assess sub-pixel information. For urban areas, the large spectral variability imposes the use of multiple endmember spectral mixture analysis techniques, which are very demanding in terms of computation time. In this study, an artificial neural network is used to inverse the pixel spectral mixture in Landsat imagery. To train the network, a spectal library was created, consisting of pure endmember spectra collected from the image and synthetic mixed spectra produced from combinations of the pure ones. Among the advantages of using a neural network is its low computational demand and its ability to capture non-linearities in the spectral mixture.