Hyperspectral Subspace Identification and Endmember Extraction by Integration of Spatial-Spectral Information

DISCLAIMER This document describes work undertaken as part of a programme of study at the Faculty of Geo-Information Science and Earth Observation of the University of Twente. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the Faculty. i ABSTRACT This research work concentrates on understanding the concepts of hyperspectral signal subspace identification or dimensionality reduction and endmember extraction by the integration of spatial information with spectrally rich hyperspectral datasets. Signal subspace identification has become an integral part of a number of hyperspectral image processing techniques in which the data dimensionality is high and there is a lot of redundant information present in the dataset. Effectively the image signal information is usually concentrated in lower dimensional subspace. Signal subspace identification enables the representation of signal vectors in this lower dimensional subspace and aids in the correct inference of the dimensionality of the dataset. Hyperspectral subspace identification by minimum error (HySime) is an eigendecomposition based technique and does not depend on any tuneable parameters. HySime initializes by determining the signal and noise correlation matrices and then representing the subspace by minimizing the mean square error between the signal projection and the noise projection. The result is an estimate of the number of spectrally distinct signal sources or the inherent dimensionality of the dataset. 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, 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 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 the spectrally similar and having similar spatial coordinates are averaged together and grouped into different endmember classes. The results highlights that HySime performs effectively in determining the number of spectrally distinct signal sources in the spaceborne hyperspectral datasets. The spatial-spectral integration results show that the endmember pixels obtained by …