Name
Title | ||
|---|---|---|
Spectral Unmixing Based On Nonnegative Matrix Factorization With Local Smoothness Constraint |
Abstract | ||
|---|---|---|
Spectral unmixing (SU) is an emerging problem in the remote sensing image processing. Since both the endmember signatures and their abundances have nonnegative values, it is a natural choice to employ the attractive nonnegative matrix factorization (NMF) methods to solve this problem. Motivated by that the abundances are sparse, the NMF with local smoothness constraint (NMF-LSC) is proposed in this paper. In the proposed method, the smoothness constraint is utilized to impose the sparseness, instead of the traditional L1-norm which is restricted by the underlying column-sum-to-one requirement of the to the abundance matrix. Simulations show the advantages of our algorithm over the compared methods. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 IEEE CHINA SUMMIT & INTERNATIONAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING | Spectral unmixing, nonnegative matrix factorization, smoothness constraint |
Field | DocType | Citations |
Endmember,Mathematical optimization,Algorithm design,Matrix (mathematics),Algorithm,Hyperspectral imaging,Non-negative matrix factorization,Artificial neural network,Smoothness,Sparse matrix,Mathematics | Conference | 2 |
PageRank | References | Authors |
0.38 | 25 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zu-yuan Yang | 1 | 312 | 24.12 |
| Liu Yang | 2 | 13 | 2.30 |
| Zhaoquan Cai | 3 | 3 | 0.74 |
| Yong Xiang | 4 | 1137 | 93.92 |