Joint hyperspectral unmixing for urban computing

Abstract

Recently, many methods for hyperspectral unmixing have been proposed. These methods are often based on nonnegative matrix factorization (NMF), which naturally inherits the non-negative advantage and is in line with the common sense of physics. Although there are many ways to perform NMF-based hyperspectral unmixing, these methods can only unmix one hyperspectral image at a time. In practice, we may often collect two or more similar hyperspectral images, and the end of the hyperspectral images of the signal could be only slightly different. Traditional NMF-based hyperspectral unmixing methods cannot take advantage of the fact that different hyper-spectral images may have similar or even the same end-element signals. Accordingly, in order to improve the performance of NMF-based hyperspectral unmixing, we present an algorithm in this paper that can process two hyperspectral images, simultaneously, and makes full use of the available information when most of the signals at the two end-points are similar. This improves the effect of end-element extraction in hyperspectral unmixing evidenced by experimental results on both synthetic and real-world data.