本文關鍵詞： 高光譜圖像 拉普拉斯支持向量機 半監(jiān)督學習　出處：《廈門大學》2014年碩士論文　論文類型：學位論文

【摘要】：基于高光譜圖像的地物分類是遙感領域的研究熱點。高光譜遙感數(shù)據(jù)最主要的特點是：將傳統(tǒng)的圖象維與光譜維信息融合為一體,在獲取地表空間圖象的同時,得到每個地物的連續(xù)光譜信息,從而實現(xiàn)依據(jù)地物光譜特征的地物成份信息反演與地物識別。高光譜內部存在著流形結構和內部數(shù)據(jù)空間信息等特點。高光譜標記樣本少而昂貴,高光譜的維度高且數(shù)據(jù)量很大,使得對高光譜的數(shù)據(jù)處理出現(xiàn)問題。本文從高光譜數(shù)據(jù)特點入手,對高光譜地物分類進行分析。主要研究成果如下： 首先,引入拉普拉斯支持向量機對高光譜數(shù)據(jù)進行地物分類。普拉斯算子能夠將高維的高光譜數(shù)據(jù)映射到低維空間中進行非線性降維,發(fā)現(xiàn)內在的流形結構。因此,將拉普拉斯應用于高光譜分類能有效的利用高光譜的特點使地物分類達到更高的準確度。在解決方案中,初始預共軛梯度優(yōu)化方法和對偶方法是兩個不同的解法。預共軛梯度解法的拉普拉斯支持向量機解法在原始方法的對偶框架上加入了基于預測穩(wěn)定性的梯度下降早期停止條件。本文將拉普拉斯預共軛梯度引入到高光譜圖像的地物分類當中,提出了利用預共軛梯度解法來對高光譜數(shù)據(jù)進行地物分類,極大地減少了訓練和分類時間以及復雜度。、 其次,針對分類器參數(shù)優(yōu)化方面,本文提出了模擬退火算法對各類分類器進行參數(shù)的優(yōu)化。在拉普拉斯的三個參數(shù)即核參數(shù)、懲罰因子、拉普拉斯因子的設置方面,模擬退火算法對參數(shù)進行優(yōu)化,與傳統(tǒng)的網格搜索優(yōu)化算法相比節(jié)省了大量的時間,特別是存在多個參數(shù)的情況下,較少了計算復雜度。 本文通過利用Indian Pine數(shù)據(jù)集中的六類地物類別進行分類試驗,同時對比了其他不同種類的分類器,顯示了本論文所提出方法的優(yōu)越性和分類的精確性。
[Abstract]:The classification of ground objects based on hyperspectral images is a hotspot in the field of remote sensing. The most important feature of hyperspectral remote sensing data is that the traditional image dimension and spectral dimension information are integrated to obtain the surface spatial image at the same time. Get the continuous spectral information for each object. In order to realize the retrieval and recognition of the composition information of the ground objects according to the spectral characteristics of the ground objects, the hyperspectral interior has the characteristics of manifold structure and internal data spatial information, and the hyperspectral labeling samples are small and expensive. The hyperspectral dimension is high and the amount of data is very large, which makes the processing of hyperspectral data appear problems. This paper starts with the characteristics of hyperspectral data, and analyzes the classification of hyperspectral features. The main research results are as follows: Firstly, Laplace support vector machine is introduced to classify hyperspectral data. Plath operator can map high-dimensional hyperspectral data to low-dimensional space for nonlinear dimensionality reduction. Therefore, the application of Laplace to hyperspectral classification can effectively make use of the characteristics of hyperspectral to achieve higher accuracy in the classification of ground objects. The initial preconjugate gradient optimization method and the dual method are two different solutions. The Laplace support vector machine method for the preconjugate gradient method adds gradient descent based on predictive stability to the dual frame of the original method. In this paper, Laplace preconjugate gradient is introduced into the classification of ground objects in hyperspectral images. A preconjugate gradient method is proposed to classify the ground objects of hyperspectral data, which greatly reduces the training and classification time and complexity. Secondly, for the optimization of classifier parameters, this paper proposes simulated annealing algorithm to optimize the parameters of all kinds of classifiers. In Laplacian, three parameters, kernel parameters, penalty factor. In the setting of Laplace factor, the simulated annealing algorithm optimizes the parameters, which saves a lot of time compared with the traditional mesh search optimization algorithm, especially in the case of multiple parameters. Less computational complexity. In this paper, by using the Indian Pine data set of the six categories of ground objects classification experiments, at the same time compared with other different types of classifiers. The advantages of the proposed method and the accuracy of classification are demonstrated.
【學位授予單位】：廈門大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP751;TP181

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