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  本文選題：高光譜圖像　切入點(diǎn)：非負(fù)矩陣分解　出處：《大連海事大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：非負(fù)矩陣分解(Non-negative matrix factorization,NMF)算法是一種盲線性光譜解混技術(shù)中的一個(gè)重要研究分支。然而,原始的NMF算法直接應(yīng)用在高光譜解混時(shí),會(huì)導(dǎo)致局部最小解,而且收斂速度慢,在此基礎(chǔ)上發(fā)展了很多的改進(jìn)算法。論文基于結(jié)合端元體積最小化的改進(jìn)算法MDC-NMF算法,考慮了端元和豐度的屬性,提出了兩種對(duì)目標(biāo)函數(shù)進(jìn)行約束的改進(jìn)的非負(fù)矩陣分解算法。一方面,利用光譜信息散度來衡量像元之間的相似性,進(jìn)而將圖像局部不變性加入到非負(fù)矩陣分解算法中,同時(shí)引入端元體積最小化約束促使單形體收斂到真實(shí)端元位置,提出了結(jié)合流型正則化和最小距離作為約束條件的非負(fù)矩陣分解算法 MMDC-NMF。另一方面考慮了圖像中像元結(jié)構(gòu)的特點(diǎn),將稀疏約束加入到非負(fù)矩陣分解算法中,對(duì)豐度矩陣進(jìn)行約束,同時(shí)加入端元距離約束,對(duì)端元矩陣進(jìn)行約束,提出了結(jié)合稀疏和最小距離作為約束條件的非負(fù)矩陣分解算法SMDC-NMF。論文通過對(duì)上述改進(jìn)后的目標(biāo)函數(shù)的構(gòu)造及迭代規(guī)則進(jìn)行推導(dǎo),分別獲得端元矩陣和豐度矩陣的優(yōu)化策略,并在模擬高光譜圖像和真實(shí)高光譜圖像上設(shè)計(jì)實(shí)現(xiàn)。模擬數(shù)據(jù)和真實(shí)數(shù)據(jù)的實(shí)驗(yàn)結(jié)果表明,所提出的兩種方法都取得了好于MDC-NMF的算法的結(jié)果,并且MMDC-NMF比SMDC-NMF適合于稀疏度較低的高光譜圖像解混,而SMDC-NMF在稀疏度較高的圖像上效果明顯。
[Abstract]:Non-negative matrix factorization (NMF) algorithm is an important branch of blind linear spectral demultiplexing. However, when the original NMF algorithm is directly applied to hyperspectral demultiplexing, it will lead to local minimum solution and slow convergence rate. On this basis, many improved algorithms are developed. In this paper, the properties of endmembers and abundance are considered based on the improved MDC-NMF algorithm, which combines with the minimization of endmember volume. Two improved nonnegative matrix decomposition algorithms are proposed to constrain the objective function. On the one hand, the spectral information divergence is used to measure the similarity between pixels, and then the local invariance of the image is added to the non-negative matrix decomposition algorithm. At the same time, the end element volume minimization constraint is introduced to make the body converge to the real end element position. A non-negative matrix decomposition algorithm MMDC-NMF, which combines flow pattern regularization and minimum distance as constraint condition, is proposed. On the other hand, considering the characteristics of pixel structure in images, sparse constraints are added to the non-negative matrix decomposition algorithm. The abundance matrix is constrained, the endmember distance constraint is added, and the endmember matrix is constrained. A nonnegative matrix decomposition algorithm, SMDC-NMF, which combines sparse and minimum distance as constraint condition, is proposed. By deducing the construction and iterative rules of the above improved objective function, the optimization strategies of endmember matrix and abundance matrix are obtained, respectively. The experimental results of the simulated and real hyperspectral images show that the proposed two methods are better than the MDC-NMF algorithm. Moreover, MMDC-NMF is more suitable than SMDC-NMF for demultiplexing of hyperspectral images with lower sparsity, while SMDC-NMF is more effective in images with higher sparsity.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP751

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本文編號(hào)：1645378