Towards streaming hyperspectral endmember extraction

A prevalent methodology for extracting pure pixels from hyperspectral images has been the use of linear-mixture geometry, which dictates that pure components must reside at the corners of a simplex enclosing all the remaining points (the mixtures). Recently, adaptations to popular algorithms for estimating the largest simplex (e.g. N-findr) have been proposed, aimed to reduce their number of iterations and so shorten the execution time. This paper goes a step further, by proposing to perform the simplex maximization in a streaming fashion, that is, by evaluating one pixel at a time without using large buffers or subsequent pixels. This is achieved by reformulating the simplex measurement in terms of distance-based geometry. Besides, a new streaming simplex-growing initialization procedure is proposed. Tested on several natural scenes, the proposed algorithm is found to yield results comparable to those produced by the reference methods.