CAJ | 학술논문

针对传统非负矩阵分解法中解空间较大、存在大量局部极小值的问题，提出了一种基于单形体体积和丰度稀疏性约束的非负矩阵分解法(Volume and Sparseness Constrained NMF，VSC-NMF)。该方法首先使用顶点成分分析法对高光谱图像进行端元提取，将其作为端元矩阵的初始值，可达到加速算法收敛的目的；然后，在目标函数中加入单形体体积最小化约束和丰度稀疏性约束，从而实现对混合像元进行较好的分解。实验结果表明，该方法不仅能有效地克服传统非负矩阵分解法的缺陷，而且能估计出精确的端元和对应的丰度，获得满意的解混效果，尤其适用于稀疏度较高的高光谱图像。
침대전통비부구진분해법중해공간교대、존재대량국부겁소치적문제，제출료일충기우단형체체적화봉도희소성약속적비부구진분해법(Volume and Sparseness Constrained NMF，VSC-NMF)。해방법수선사용정점성분분석법대고광보도상진행단원제취，장기작위단원구진적초시치，가체도가속산법수렴적목적；연후，재목표함수중가입단형체체적최소화약속화봉도희소성약속，종이실현대혼합상원진행교호적분해。실험결과표명，해방법불부능유효지극복전통비부구진분해법적결함，이차능고계출정학적단원화대응적봉도，획득만의적해혼효과，우기괄용우희소도교고적고광보도상。
To solve the problem of large solution space and a mass of local minima in the traditional non-negative matrix factorization (NMF), a volume and sparseness constrained NMF (VSC-NMF) algorithm was proposed. Firstly, end -members extracted by vertex component analysis (VCA) in hyperspectral image were taken as initialization of end -member matrix so as to accelerate the convergence speed. Then, the traditional NMF was extended by incorporating the minimum volume constraint and abundance′s sparseness constraint to achieve better separation of mixed pixels. The experimental results on synthetic and real data illustrate that the proposed algorithm can overcome the shortcomings of traditional NMF and obtain more accurate end-members and corresponding abundance, especially in sparser hyperspectral image.