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Maximum Likelihood Estimation Based Nonnegative Matrix Factorization for Hyperspectral Unmixing
| Content Provider | MDPI |
|---|---|
| Author | Jiang, Qin Dong, Yifei Peng, Jiangtao Yan, Mei Sun, Yi |
| Copyright Year | 2021 |
| Description | Hyperspectral unmixing (HU) is a research hotspot of hyperspectral remote sensing technology. As a classical HU method, the nonnegative matrix factorization (NMF) unmixing method can decompose an observed hyperspectral data matrix into the product of two nonnegative matrices, i.e., endmember and abundance matrices. Because the objective function of NMF is the traditional least-squares function, NMF is sensitive to noise. In order to improve the robustness of NMF, this paper proposes a maximum likelihood estimation (MLE) based NMF model (MLENMF) for unmixing of hyperspectral images (HSIs), which substitutes the least-squares objective function in traditional NMF by a robust MLE-based loss function. Experimental results on a simulated and two widely used real hyperspectral data sets demonstrate the superiority of our MLENMF over existing NMF methods. |
| Starting Page | 2637 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs13132637 |
| Journal | Remote Sensing |
| Issue Number | 13 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-07-05 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Imaging Science Hyperspectral Unmixing Maximum Likelihood Estimation Nonnegative Matrix Factorization |
| Content Type | Text |
| Resource Type | Article |
