Spectral unmixing is a common task in hyperspectral data analysis. In order to sufficiently spectrally unmix the data, three key steps must be accomplished: Estimate the number of endmembers (EMs), identify the EMs, and then unmix the data. Several different statistical and geometrical approaches have been developed for all steps of the unmixing process. However, many of these methods rely on using the full image to estimate the number and extract the EMs from the background data. In this paper, spectral unmixing is accomplished using a spatially adaptive approach. Linear unmixing is performed per pixel with EMs identified at the local level, but global abundance maps are created by clustering the locally determined EMs into common groups. Results show that the unmixing residual error of each pixel's spectrum from real data, estimated from the spatially adaptive methodology, is reduced when compared to a global scale EM estimation and linear unmixing methodology. The component algorithms of the new spatially adaptive approach, which complete the three key unmixing steps, can be interchanged while maintaining spatial information, making this new methodology modular. A final advantage of the spatially adaptive spectral unmixing methodology is the user-defined spatial scale size.
Linear spectral unmixing and endmember selection are two of the many tasks that can be accomplished using hyperspectral imagery. The quality of the unmixing results depends on an accurate estimate of the number of endmembers used in the analysis. Too many estimated endmembers produce over fitting of the spectral unmixing results; too few estimated endmembers produce spectral unmixing results with large residual errors. Several statistical and geometrical approaches have been developed to estimate the number of endmembers, but many of these approaches rely on using the global dataset. The global approach does not take into consideration local endmember variability, which is of particular interest in high-spatial resolution imagery. Here, the number of endmembers within local image tiles is estimated by using a novel, spatially adaptive approach. Each pixel is unmixed using the locally identified endmembers and global abundance maps are generated by clustering these locally derived endmembers. Comparisons are made between this new approach and an established global method that uses PCA to estimate the number of endmembers and SMACC to identify the spectra. Multiple images with varying spatial resolution are used in the comparison of methodologies and conclusions are drawn based on per-pixel residual unmixing errors.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.