Unmixing urban hyperspectral imagery with a Gaussian mixture model on endmember variability
This work addresses hyperspectral unmixing for urban analysis, offering a method with slow model growth relative to library size, but it appears incremental as it builds on existing distribution-based approaches.
The paper tackled the problem of unmixing urban hyperspectral imagery by modeling pixels as linear combinations of endmembers from Gaussian mixture models, using spectral libraries for parameter estimation. It showed that GMM performed best among distribution-based methods, achieving comparable accuracy to MESMA with results validated on AVIRIS data over Santa Barbara using 64 ROIs and ground truth from 1 m images.
In this paper, we model a pixel as a linear combination of endmembers sampled from probability distributions of Gaussian mixture models (GMM). The parameters of the GMM distributions are estimated using spectral libraries. Abundances are estimated based on the distribution parameters. The advantage of this algorithm is that the model size grows very slowly as a function of the library size. To validate this method, we used data collected by the AVIRIS sensor over the Santa Barbara region: two 16 m spatial resolution and two 4 m spatial resolution images. 64 validated regions of interest (ROI) (180 m by 180 m) were used to assess estimate accuracy. Ground truth was obtained using 1 m images leading to the following 6 classes: turfgrass, non-photosynthetic vegetation (NPV), paved, roof, soil, and tree. Spectral libraries were built by manually identifying and extracting pure spectra from both resolution images, resulting in 3,287 spectra at 16 m and 15,426 spectra at 4 m. We then unmixed ROIs of each resolution using the following unmixing algorithms: the set-based algorithms MESMA and AAM, and the distribution-based algorithms GMM, NCM, and BCM. The original libraries were used for the distribution-based algorithms whereas set-based methods required a sophisticated reduction method, resulting in reduced libraries of 61 spectra at 16 m and 95 spectra at 4 m. The results show that GMM performs best among the distribution-based methods, producing comparable accuracy to MESMA, and may be more robust across datasets.