本文選題：高光譜遙感　切入點(diǎn)：礦物識別　出處：《吉林大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：高光譜遙感（Hyperspectral Remote Sensing）是在電磁波譜的可見光、近紅外、中紅外和熱紅外波段范圍內(nèi)，利用成像光譜儀[1]獲取許多非常窄的光譜連續(xù)的影像數(shù)據(jù)的技術(shù)[2]。由于高光譜遙感技術(shù)在分辨率方面的巨大優(yōu)勢和相關(guān)技術(shù)的日趨成熟，其應(yīng)用領(lǐng)域也日益廣泛，世界各國對遙感領(lǐng)域的發(fā)展都十分重視。 礦物識別在地質(zhì)學(xué)中是一個非常重要的課題。也是高光譜遙感技術(shù)最能發(fā)揮其優(yōu)勢的應(yīng)用領(lǐng)域之一。所謂礦物識別，是指利用一定的方法和手段能夠精確的識別野外獲得的礦物樣本的種類。高光譜技術(shù)的應(yīng)用，使遙感地質(zhì)由識別地礦種類發(fā)展到識別單礦物以至礦物的化學(xué)成分。由于礦物成因的復(fù)雜性以及礦物樣本的多樣性，其反射波譜受到自身化學(xué)成分及晶體結(jié)構(gòu)的影像，更受到其他礦物光譜混合其中等因素的影響。另外，由于同一種礦物因形成過程和所處地理?xiàng)l件的影響，其化學(xué)成分、晶體結(jié)構(gòu)及光譜特征會仍然有所不同，使其礦物光譜具有地理區(qū)域特征；并且由于測量條件的不同，礦物也會隨光照強(qiáng)度、背景顏色、風(fēng)化程度、顆粒大小等因素的原因呈現(xiàn)出不同的光譜特征，這也導(dǎo)致了“同譜異物”和“同物異譜”的現(xiàn)象[5]大量存在，因此目前基于波形匹配的礦物識別方法并不能取得令人滿意的效果，在進(jìn)行礦物識別過程中極易出現(xiàn)混淆和誤判現(xiàn)象。 數(shù)據(jù)庫技術(shù)的發(fā)展，使我們有了利用海量數(shù)據(jù)進(jìn)行分析的條件。但是，要想從大量的數(shù)據(jù)中提取有用的信息和規(guī)律，并不是意見容易的事情。處理海量數(shù)據(jù)無論對于軟件還是硬件都有著較高的要求。因此人們迫切需要處理海量數(shù)據(jù)的方法，這導(dǎo)致了數(shù)據(jù)挖掘技術(shù)的誕生和發(fā)展。 數(shù)據(jù)挖掘（Data Mining）是指從大量的數(shù)據(jù)中自動搜索隱藏于其中的有著特殊關(guān)系性的信息的過程。作為一個新興的研究領(lǐng)域，自從20世紀(jì)80年代以來，數(shù)據(jù)挖掘已經(jīng)取得了顯著進(jìn)展并且涵蓋了廣泛的應(yīng)用。與傳統(tǒng)的數(shù)據(jù)分析相比，數(shù)據(jù)挖掘技術(shù)更加關(guān)注“是什么”而不再關(guān)注“為什么”，即在有大量可靠數(shù)據(jù)的前提下，我們可以直接獲得兩種事物之間的相關(guān)性，而不必去關(guān)注為什么會有這種關(guān)聯(lián)。如今，數(shù)據(jù)挖掘已經(jīng)被應(yīng)用到了眾多的領(lǐng)域，例如金融、零售、物流等商業(yè)領(lǐng)域以及氣象、地質(zhì)等科研領(lǐng)域。隨著數(shù)據(jù)挖掘領(lǐng)域的不斷發(fā)展，相應(yīng)的技術(shù)會越來越成熟，，數(shù)據(jù)挖掘技術(shù)會在越來越多的領(lǐng)域發(fā)揮出重大作用。 高光譜遙感技術(shù)的發(fā)展，使我們能夠利用高光譜遙感數(shù)據(jù)來處理某些傳統(tǒng)的問題。但是由于高光譜遙感擁有海量數(shù)據(jù)的特點(diǎn)，傳統(tǒng)的算法已經(jīng)不能滿足遙感數(shù)據(jù)處理的需要[4]。而數(shù)據(jù)挖掘技術(shù)的發(fā)展為高光譜遙感數(shù)據(jù)的處理開辟了一個新的方向。通過對海量的高光譜數(shù)據(jù)進(jìn)行數(shù)據(jù)清理、數(shù)據(jù)集成、數(shù)據(jù)選擇、數(shù)據(jù)變換、數(shù)據(jù)挖掘以及模式評估，我們可以挖掘出對我們有用的知識和規(guī)律。 本文結(jié)合高光譜數(shù)據(jù)的特點(diǎn)與相關(guān)特征參數(shù),以樸素貝葉斯、K-均值等分類聚類算法為基礎(chǔ)，開發(fā)適合于高光譜數(shù)據(jù)處理及應(yīng)用的光譜建模、光譜匹配技術(shù)，并且采用ENVIi軟件自帶的高光譜數(shù)據(jù)庫開展高光譜數(shù)據(jù)挖掘技術(shù)研究，探索高光譜遙感數(shù)據(jù)在礦物識別、礦物特征提取等方面的應(yīng)用潛力。并且希望能將多種數(shù)據(jù)挖掘算法結(jié)合在一起，形成一個針對高光譜數(shù)據(jù)的算法體系，并且利用得到的地物波譜數(shù)據(jù)，充實(shí)現(xiàn)有的標(biāo)準(zhǔn)波譜庫，推動高光譜遙感領(lǐng)域的研究。
[Abstract]:Hyperspectral remote sensing (Hyperspectral Remote Sensing) is in the electromagnetic spectrum of visible light and near infrared, mid infrared and thermal infrared range, using imaging spectrometer [1] to obtain image data of many very narrow continuous spectrum technology [2]. due to the high spectral remote sensing technology in distinguishing great advantages and related technical aspects of the mature rate and its applications have become increasingly widespread, the world development of the field of remote sensing is very seriously.
Mineral identification is a very important topic in geology. One of the applications of hyperspectral remote sensing technology is the most can exert its advantages. The so-called mineral identification, refers to the use of certain methods and means can accurately obtain the wild type mineral samples. Application of hyperspectral technology, the remote sensing geological identification mineral species developed to identify single mineral and Mineral chemical composition. Because of the complexity of the genesis of mineral and mineral samples diversity, the spectral reflectance is restricted by its chemical composition and crystal structure of the image, more affected by other factors such as the mineral spectral mixing effect. In addition, because of the same minerals due to the formation of the process and effect the geographical conditions, the chemical composition, crystal structure and spectral characteristics will still vary, the mineral spectrum has geographical features; and the measurement conditions Different minerals will be weathered with light intensity, background color, reason, particle size and other factors show different spectral features, which also lead to the same spectrum "and" synonyms spectrum "phenomenon of the existence of a large number of [5], so the current based on waveform matching recognition method and mineral has not been satisfactory results in the process of mineral identification are prone to confusion and misjudgment.
The development of database technology, we have analyzed the conditions of using large amounts of data. However, in order to extract useful information and rules from a large amount of data, is not easy. Massive data processing for both hardware and software have higher requirements. Therefore, there is an urgent need to deal with massive data and this led to the birth and development of the data mining technology.
Data mining (Data Mining) refers to data from a large number of automatic search hidden in the process with a special relationship of information. As a new research field, since 1980s, data mining has made significant progress and covers a wide range of applications. Compared with traditional data analysis, data mining technology pays more attention to "what" and "why", that is no longer concerned in a large amount of reliable data, we can directly obtain the correlation between two things, without having to pay attention to why this association. Now, data mining has been applied to many fields, such as finance, retail, logistics business and meteorological, geological and other research fields. With the continuous development of the field of data mining, the technology will be more and more mature, data mining technology will be more and more The field has played a major role.
The development of hyperspectral remote sensing technology, which enables us to handle some traditional problems by using hyperspectral remote sensing data. But due to the high spectral remote sensing has a mass of data, traditional algorithms can not meet the needs of remote sensing data processing and processing of [4]. data mining technology for the development of hyperspectral remote sensing data has opened up a new the direction of data cleaning, the hyperspectral data of massive data integration, data selection, data transformation, data mining and pattern, we can dig out the knowledge and rules useful to us.
According to the characteristics of hyperspectral data and relevant parameters, to Naive Bayesian, mean K- classification clustering algorithm based on spectral modeling for high spectral data processing and application of spectral matching technique, and using the hyperspectral data library of ENVIi software to carry out the hyperspectral data mining technology research and exploration of hyperspectral remote sensing the data in the application potential of mineral mineral recognition, feature extraction and so on. And I want to be a variety of data mining algorithms together to form a system of algorithm for hyperspectral data, and using the spectral data obtained, enrich the existing standard spectrum library, promote the research field of hyperspectral remote sensing.

【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP751

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 王雷,馮學(xué)智,都金康;遙感影像分類與地學(xué)知識發(fā)現(xiàn)的集成研究[J];地理研究;2001年05期

2 王錚,吳健平,鄧悅,王凌云,熊云波;城市土地利用演變信息的數(shù)據(jù)挖掘——以上海市為例[J];地理研究;2002年06期

3 張振飛,胡光道,楊明國;基于進(jìn)化策略的CHC遺傳算法及巖性波譜識別[J];地球科學(xué);2003年03期

4 張治英,徐德忠,周曉農(nóng),周云,孫志東,張波,龔自力,劉士軍;應(yīng)用LANDSAT ETM+圖像監(jiān)測江寧縣江灘釘螺孳生地[J];第四軍醫(yī)大學(xué)學(xué)報;2003年02期

5 蘇理宏,李小文,王錦地,唐世浩;典型地物波譜知識庫建庫與波譜服務(wù)的若干問題[J];地球科學(xué)進(jìn)展;2003年02期

6 王潤生;甘甫平;閆柏琨;楊蘇明;王青華;;高光譜礦物填圖技術(shù)與應(yīng)用研究[J];國土資源遙感;2010年01期

7 許衛(wèi)東,尹球,匡定波;地物光譜匹配模型比較研究[J];紅外與毫米波學(xué)報;2005年04期

8 李雄飛,宋海玉,謝忠時,任巖,苑森淼;圖像數(shù)據(jù)挖掘模型與方法[J];吉林工業(yè)大學(xué)學(xué)報(工學(xué)版);2002年01期

9 顏雪松,蔡之華;一種基于圖像的關(guān)聯(lián)規(guī)則發(fā)現(xiàn)算法的研究[J];計算機(jī)工程與應(yīng)用;2003年02期

10 白繼偉,趙永超,張兵,童慶禧,鄭蘭芬;基于包絡(luò)線消除的高光譜圖像分類方法研究[J];計算機(jī)工程與應(yīng)用;2003年13期

本文編號：1630367