KEYWORDS: Hyperspectral imaging, Reconstruction algorithms, Image analysis, Data modeling, Signal to noise ratio, Chemical elements, Electrical engineering, Remote sensing, Current controlled current source, Computer programming
A great challenge in hyperspectral image analysis is decomposing a mixed pixel into a collection of endmembers and their corresponding abundance fractions. This paper presents an improved implementation of Barycentric Coordinate approach to unmix hyperspectral images, integrating with the Most-Negative Remove Projection method to meet the abundance sum-to-one constraint (ASC) and abundance non-negativity constraint (ANC). The original barycentric coordinate approach interprets the endmember unmixing problem as a simplex volume ratio problem, which is solved by calculate the determinants of two augmented matrix. One consists of all the members and the other consist of the to-be-unmixed pixel and all the endmembers except for the one corresponding to the specific abundance that is to be estimated. In this paper, we first modified the algorithm of Barycentric Coordinate approach by bringing in the Matrix Determinant Lemma to simplify the unmixing process, which makes the calculation only contains linear matrix and vector operations. So, the matrix determinant calculation of every pixel, as the original algorithm did, is avoided. By the end of this step, the estimated abundance meet the ASC constraint. Then, the Most-Negative Remove Projection method is used to make the abundance fractions meet the full constraints. This algorithm is demonstrated both on synthetic and real images. The resulting algorithm yields the abundance maps that are similar to those obtained by FCLS, while the runtime is outperformed as its computational simplicity.
Nonlinear spectral unmixing constitutes an important field of research for hyperspectral imagery. An unsupervised nonlinear spectral unmixing algorithm, namely multiple kernel constrained nonnegative matrix factorization (MKCNMF) is proposed by coupling multiple-kernel selection with kernel NMF. Additionally, a minimum endmemberwise distance constraint and an abundance smoothness constraint are introduced to alleviate the uniqueness problem of NMF in the algorithm. In the MKCNMF, two problems of optimizing matrices and selecting the proper kernel are jointly solved. The performance of the proposed unmixing algorithm is evaluated via experiments based on synthetic and real hyperspectral data sets. The experimental results demonstrate that the proposed method outperforms some existing unmixing algorithms in terms of spectral angle distance (SAD) and abundance fractions.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.