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  本文選題：高光譜遙感　切入點：波段選擇　出處：《浙江大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：高光譜遙感是近年來發(fā)展起來的具有高光譜分辨率的遙感科學(xué)和技術(shù),對地物的分辨識別能力非常高,但由于其高分辨率也對數(shù)據(jù)處理帶來了一系列的挑戰(zhàn),主要是數(shù)據(jù)量大,冗余信息多,某些波段噪聲含量大,這對數(shù)據(jù)處理的效率和精度造成一定的影響。本文針對高光譜遙感的一系列問題,對不改變波段物理意義又能達(dá)到縮小數(shù)據(jù)源的波段選擇算法展開了研究。 波段選擇算法主要分為監(jiān)督和非監(jiān)督兩類,對于一般無先驗知識的非監(jiān)督的波段選擇算法來說,信息量大,獨立性好是選擇波段的主要原則。在眾多波段選擇算法中,線性預(yù)測波段選擇算法是相對來說原理明確、效率高、結(jié)果有效的算法。但經(jīng)分析,該算法還存在一系列的缺點,造成波段選擇的結(jié)果非最優(yōu),效率也有待提高。 針對原線性預(yù)測波段選擇算法的三個主要問題,本文進(jìn)行了比較徹底的改進(jìn)。第一是噪聲波段的去除算法,提出了通過計算圖像小波域的熵,估計出波段圖像的噪聲并將噪聲較大的波段去除的思路；第二是初始波段選擇算法的改進(jìn),用偏度峰度、互信息、K-L散度衡量波段的信息量大小,同時用信息量和獨立性兩個準(zhǔn)則來選出初始波段,既考慮了波段的信息量,又提高了初始波段選擇的效率；第三是線性預(yù)測后續(xù)波段選擇的改進(jìn),每次迭代都去除線性預(yù)測誤差最小的波段,這樣可以逐漸減少數(shù)據(jù)源,提高波段選擇的效率。 針對以上改進(jìn)思路,本文分別用高光譜圖像處理非常重要的分類和解混兩個應(yīng)用來對算法進(jìn)行了實驗驗證,實驗中采用支持向量機、最近鄰算法進(jìn)行分類,用非負(fù)矩陣分解進(jìn)行解混,兩個實驗都從精度和效率兩方面驗證了改進(jìn)的線性預(yù)測波段選擇算法的優(yōu)越性,證明了這是一種有效的高光譜圖像數(shù)據(jù)降維方法。
[Abstract]:Hyperspectral remote sensing is a kind of remote sensing science and technology with high spectral resolution developed in recent years. Its ability to distinguish and recognize ground objects is very high. However, its high resolution also brings a series of challenges to data processing, mainly because of the large amount of data. There are many redundant information and high noise content in some bands, which has a certain effect on the efficiency and precision of data processing. This paper aims at a series of problems in hyperspectral remote sensing. The band selection algorithm which can reduce the data source without changing the physical meaning of the band is studied. Band selection algorithms are mainly divided into two categories: supervised and unsupervised. For general unsupervised band selection algorithms without prior knowledge, large amount of information and good independence are the main principles of band selection. The linear predictive band selection algorithm is relatively clear in principle, high in efficiency and effective in the result. However, the analysis shows that the algorithm still has a series of shortcomings, resulting in the result of band selection is not optimal, and the efficiency needs to be improved. Aiming at the three main problems of the original linear prediction band selection algorithm, this paper makes a relatively thorough improvement. First, the noise band removal algorithm is proposed, and the entropy of the image wavelet domain is calculated. The second is the improvement of the initial band selection algorithm, which uses the bias kurtosis and mutual information K-L divergence to measure the information content of the band. At the same time, the information quantity and independence criteria are used to select the initial band, which not only considers the information content of the band, but also improves the efficiency of the initial band selection. Each iteration removes the band with the least linear prediction error, which can gradually reduce the data source and improve the efficiency of band selection. In view of the above improved ideas, this paper uses the very important classification and mixing of hyperspectral image processing to verify the algorithm. In the experiment, support vector machine and nearest neighbor algorithm are used to classify the algorithm. The nonnegative matrix decomposition is used to solve the problem. Both experiments verify the superiority of the improved linear predictive band selection algorithm in terms of accuracy and efficiency. It is proved that this algorithm is an effective method for dimensionality reduction of hyperspectral image data.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP751

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本文編號：1645452