本文選題：高光譜圖像解混 + 非負(fù)矩陣分解　； 參考：《西安電子科技大學(xué)》2015年碩士論文

【摘要】：由于遙感數(shù)據(jù)集的空間分辨率,遙感成像儀在自然環(huán)境中的收集的光譜信號必然是各種物質(zhì)的混合物。因此,準(zhǔn)確估計需要光譜解混�；旌舷袼胤纸夥椒ò凑账捎玫姆纸饽Ｐ�,大致可以分為基于線型光譜混合模型的分解方法和基于非線性光譜混合模型的分解方法。本文考慮了豐度的稀疏性、空間信息和建模的多樣性,改進了現(xiàn)有的非線性解混方法,具體如下:1.高光譜數(shù)據(jù)的相關(guān)性會導(dǎo)致數(shù)據(jù)的稀疏性,而且每個像素并非包含所有端元。而大多數(shù)現(xiàn)有的非線性解混算法沒有考慮數(shù)據(jù)的稀疏信息。針對非線性解混算法沒有考慮數(shù)據(jù)的稀疏性,提出了稀疏約束的廣義雙線性模型解混。廣義雙線性模型(GBM)已被廣泛用于非線性高光譜圖像解混。高光譜數(shù)據(jù)的高度相關(guān)性導(dǎo)致了豐度的稀疏性。目前正則化方法通常用來約束豐度的稀疏性,目的通過將豐度矩陣的稀疏性約束添加到GBM模型中,拓展semi-NMF,得到L_（1/2）約束半非負(fù)矩陣分解(L_（1/2）-semi-NMF)算法來估計豐度和非線性系數(shù)。將GBM分成的線性部分和二階部分,并分別使用迭代優(yōu)化算法優(yōu)化�？朔税敕秦�(fù)矩陣分解算法容易陷入局部最小點的缺點,收斂速度加快且不易限于局部最優(yōu)解。在高光譜合成數(shù)據(jù)和真實數(shù)據(jù)上的實驗結(jié)果表明:該方法提高了解混的穩(wěn)定性和結(jié)果的正確性。2.在雙線性場景中,植被和土壤之間通常發(fā)生多重散射,而包含植被和土壤等物質(zhì)的高光譜圖像在邊界區(qū)域才可能發(fā)生雙線性混合,考慮了圖像區(qū)域差異性,提出基于區(qū)域自適應(yīng)分割的高光譜圖像解混方法。先用K均值聚類方法對高光譜數(shù)據(jù)聚類,將圖像分割為勻質(zhì)區(qū)域和細節(jié)區(qū)域。勻質(zhì)區(qū)域采用線性模型,用稀疏約束的非負(fù)矩陣分解方法解混,細節(jié)區(qū)域采用廣義雙線性模型,用稀疏約束的半非負(fù)矩陣分解方法解混,很好的保持了雙線性豐度的邊緣信息。對比實驗表明:所提出的方法有效提高了高光譜遙感圖像的解混準(zhǔn)確率。3.大多數(shù)現(xiàn)有的稀疏NMF算法對于高光譜解混只考慮歐幾里得結(jié)構(gòu)的高光譜數(shù)據(jù)空間。事實上,高光譜數(shù)據(jù)更可能位于一條嵌入高維空間的低維流形。針對非線性解混算法沒有考慮高光譜數(shù)據(jù)內(nèi)在的流形結(jié)構(gòu),提出了圖約束的廣義雙線性模型解混。添加的圖正則可以保持原始圖像和豐度圖之間的密切聯(lián)系,改進的方法能改善解混性能。
[Abstract]:Because of the spatial resolution of remote sensing data sets, the spectral signals collected by remote sensing imagers in the natural environment must be mixtures of various substances. Therefore, accurate estimation requires spectral unmixing. According to the decomposition model, the mixed pixel decomposition method can be divided into linear spectral mixed model decomposition method and nonlinear spectral mixed model decomposition method. In this paper, the sparsity of abundance, the diversity of spatial information and modeling are considered, and the existing nonlinear demultiplexing methods are improved as follows: 1. The correlation of hyperspectral data leads to data sparsity, and not every pixel contains all endpoints. However, most of the existing nonlinear unmixing algorithms do not consider the sparse information of the data. In this paper, a generalized bilinear model with sparse constraints is proposed to solve the problem that the data sparsity is not considered in the nonlinear de-mixing algorithm. The generalized bilinear model (GBM) has been widely used in nonlinear hyperspectral image demultiplexing. The high correlation of hyperspectral data leads to the sparsity of abundance. At present, regularization methods are usually used to constrain the sparsity of abundance. Aim to estimate the abundance and nonlinear coefficients by adding the sparse constraint of abundance matrix to the GBM model and extending the semi-NMFs to obtain the LSP 1 / 2) constrained semi-negative matrix decomposition algorithm. The GBM is divided into linear part and second order part, and the iterative optimization algorithm is used respectively. It overcomes the shortcoming that semi-nonnegative matrix factorization algorithm is easy to fall into the local minimum point, and the convergence speed is accelerated and is not easy to be limited to the local optimal solution. The experimental results on hyperspectral synthetic data and real data show that the proposed method improves the stability of the mixture and the correctness of the results. In bilinear scenarios, multiple scattering usually occurs between vegetation and soil, while hyperspectral images containing vegetation and soil are likely to be bilinear mixed in the boundary region, taking into account the regional differences of the images. A method of hyperspectral image de-mixing based on region adaptive segmentation is proposed. The K-means clustering method is used to cluster the hyperspectral data, and the image is divided into homogeneous region and detail region. Linear model is used in homogeneous region, non-negative matrix decomposition method with sparse constraint is used to solve the problem, generalized bilinear model is used in detail region and semi-non-negative matrix decomposition method with sparse constraint is used to solve the problem. The edge information of bilinear abundance is well preserved. The experimental results show that the proposed method can effectively improve the accuracy of hyperspectral remote sensing images. Most existing sparse NMF algorithms only consider the hyperspectral data space of Euclidean structure for hyperspectral demultiplexing. In fact, hyperspectral data are more likely to be located in a low dimensional manifold embedded in a high dimensional space. In this paper, a graph constrained generalized bilinear model is proposed to solve the problem that the nonlinear unmixing algorithm does not take into account the intrinsic manifold structure of hyperspectral data. The added graph regularization can maintain the close relationship between the original image and the abundance graph, and the improved method can improve the demultiplexing performance.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TP751

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