| Yu Yue (于钺),Sun Weidong.[J].高技术通讯(英文),2012,18(4):333~342 |
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| Minimum distance constrained nonnegative matrix factorization for hyperspectral data unmixing |
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| DOI： |
| 中文关键词: |
| 英文关键词: hyperspectral data, nonnegative matrix factorization (NMF), spectral unmixing, convex function, projected gradient (PG) |
| 基金项目: |
| Author Name | Affiliation | | Yu Yue (于钺) | | | Sun Weidong | |
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| 中文摘要: |
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| 英文摘要: |
| This paper considers a problem of unsupervised spectral unmixing of hyperspectral data. Based on the Linear Mixing Model (LMM), a new method under the framework of nonnegative matrix factorization (NMF) is proposed, namely minimum distance constrained nonnegative matrix factorization (MDC-NMF). In this paper, firstly, a new regularization term, called endmember distance (ED) is considered, which is defined as the sum of the squared Euclidean distances from each endmember to their geometric center. Compared with the simplex volume, ED has better optimization properties and is conceptually intuitive. Secondly, a projected gradient (PG) scheme is adopted, and by the virtue of ED, in this scheme the optimal step size along the feasible descent direction can be calculated easily at each iteration. Thirdly, a finite step (no more than the number of endmembers) terminated algorithm is used to project a point on the canonical simplex, by which the abundance nonnegative constraint and abundance sum-to-one constraint can be accurately satisfied in a light amount of computation. The experimental results, based on a set of synthetic data and real data, demonstrate that, in the same running time, MDC-NMF outperforms several other similar methods proposed recently. |
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