No abstract

Linear unmixing is a blind source separation problem that decomposes a hyperspectral image into the spectra of the material constituents of the scene and the abundance maps of those materials across that scene. A novel method for determining the material spectra from within the scene, AGES, is proposed based on the positional information contained within abundances generated by additivity-constrained inversion. This new approach is compared on both simulated and real data sets to the well established N-FINDR algorithm, comparing favorably in terms of computational complexity with the existing algorithm without significantly sacrificing accuracy. In addition, the algorithm has some desirable properties inherent in such an approach.

The N-FINDR algorithm for unmixing hyperspectral data is both popular and successful. However, opportunities for improving the algorithm exist, particularly to reduce its computational expense. Two approaches to achieve this are examined. First, the redundancy inherent in the determinant calculations at the heart of N-FINDR is reduced using an LDU decomposition to form two new algorithms, one based on the original N-FINDR algorithm and one based on the closely related Sequential N-FINDR algorithm. The second approach lowers complexity by reducing the repetition of the volume calculations by removing pixels unlikely to represent pure materials. This is accomplished at no additional cost through the reuse of the volume calculations inherent in the Sequential N-FINDR algorithm. Various thresholding methods for excluding pixels are considered. The impact of these modifications on complexity and the accuracy is examined on simulated and real data showing that the LDU-based approaches save considerable complexity, while pixel reduction methods, with appropriate threshold selection, can produce a favorable complexity-accuracy trade-off.