Spatial-spectral endmember extraction for spaceborne hyperspectral data

Most endmember extraction algorithms are based on the spectral properties of the dataset only to discriminate between the pixels. Endmembers with distinct spectral profiles or high spectral contrast are easier to detect, whereas the endmembers having low spectral contrast with respect to the whole image are difficult to determine. The spatial-spectral integration approach searches for endmembers by analyzing the image in subsets such that it increases the local spectral contrast of the low contrast endmembers and increases their odds of selection. Spatial spectral integration process utilizes Hyperspectral subspace identification by minimum error (HySime) to determine a set of locally defined eigenvectors explaining the maximum variability of the subsets of the image. The image data is then projected onto these locally defined eigenvectors which produces a set of candidate endmember pixels. The candidate endmember pixels, that are spectrally similar and having similar spatial coordinates, are averaged together and grouped into different endmember classes. The method is applied to spaceborne hyperspectral dataset to illustrate the effects of using spatial measures in the process of endmember extraction. The spatial-spectral integration results show that the endmember pixels obtained by imposing spatial constraints are cleaner and more representative of the land use land cover classes.