發(fā)布時間：2018-03-14 07:08

  本文選題：遙感圖像分類　切入點：特征提取　出處：《北方民族大學》2017年碩士論文　論文類型：學位論文

【摘要】：隨著遙感影像技術和信息技術的迅速發(fā)展,遙感影像數(shù)據(jù)量呈現(xiàn)快速增長趨勢。面對海量的遙感數(shù)據(jù),如何利用計算機按照一定規(guī)則自動對影像進行分類成為具有挑戰(zhàn)性的研究課題。傳統(tǒng)的方法是目視解譯,該方法需要豐富的專業(yè)經(jīng)驗知識,又需要充足的戶外實地調(diào)查資料,而且這種識別方法建立在具有一定的先驗知識基礎上,所以識別難度大,效率較低。高光譜遙感技術又稱為成像光譜遙感技術,具有圖像和光譜信息融合的特點,高光譜圖像上的每一個像元分別對應一條獨特的光譜曲線,因此利用這一性質(zhì)可以根據(jù)地物的光譜反射率來識別遙感影像中的地物類型。同時高光譜圖像兼具光學特性和光譜識別能力,已經(jīng)成為遙感影像領域廣泛研究與應用的焦點。遙感圖像分類的應用在遙感圖像研究中具有重要意義。基于單個特征分類算法對于遙感圖像的分類能力是比較有限的,因此為了提高高光譜遙感圖像分類的精度,本文提出了基于多特征融合的高光譜遙感分類方法,主要研究工作如下:1.高光譜遙感圖像分類方法相關概述。分析了高光譜遙感圖像的特征,對前人的高光譜圖像特征分類方法進行分析和總結,比較單特征分類方法的不足,提出了基于多特征融合的高光譜遙感圖像分類研究。2.經(jīng)過分析研究發(fā)現(xiàn),遙感圖像分類可以從兩方面改進:一方面,改進特征;另一方面,改進分類算法。因此本文提出了基于這兩方面改進的算法�；谔卣鞲倪M方法是以現(xiàn)有的特征作為基礎,詳細介紹紋理特征,直方圖特征,降維特征并且利用各類特征的優(yōu)勢,對上述三類特征進行歸一化融合,獲得融合特征,接著使用AdaBoost集成的三種分類算法Real AdaBoost,Gentle AdaBoost,Modest AdaBoost分別對高光譜遙感數(shù)據(jù)單特征和融合特征進行分類,并且對分類結果進行對比�；诜诸愃惴ǜ倪M方法是在RVM算法基礎上,將局部二值化對比算法(LBPC)和形態(tài)學算法(Morphologic Algorithm)進行改進獲得L-M Algorithm算法,接著將改進的算法嵌入到RVM算法,然后獲得融合特征分類結果。為了使實驗更加充分,本文還利用SVM和AdaBoost算法進行分類對比實驗。3.分類算法選擇。分類算法性能的優(yōu)劣直接決定著分類效果的好壞,因此,分類算法的選擇是高光譜圖像分類中的關鍵一步。實驗結果表明,相比于單特征分類精度,融合算法獲得的分類精度更高。因此可以得出本文提出的融合算法對于特征分類效果更可靠,更精準的結論。
[Abstract]:With the rapid development of remote sensing image technology and information technology, the amount of remote sensing image data is increasing rapidly. How to use the computer to classify images automatically according to certain rules has become a challenging research topic. The traditional method is visual interpretation, which requires rich professional experience and sufficient outdoor field investigation data. And this recognition method is based on a certain priori knowledge, so the recognition is difficult and inefficient. The hyperspectral remote sensing technology, also called imaging spectral remote sensing technology, has the characteristics of image and spectral information fusion. Each pixel on a hyperspectral image corresponds to a unique spectral curve, Therefore, this property can be used to identify the types of ground objects in remote sensing images according to the spectral reflectivity of ground objects. At the same time, hyperspectral images have both optical properties and spectral recognition capabilities. The application of remote sensing image classification is of great significance in remote sensing image research. The classification ability of remote sensing image based on single feature classification algorithm is relatively limited. Therefore, in order to improve the accuracy of hyperspectral remote sensing image classification, a hyperspectral remote sensing classification method based on multi-feature fusion is proposed in this paper. The main research work is as follows: 1. Overview of hyperspectral remote sensing image classification methods. The features of hyperspectral remote sensing images are analyzed, and the former hyperspectral image feature classification methods are analyzed and summarized, and the shortcomings of single feature classification methods are compared. The hyperspectral remote sensing image classification based on multi-feature fusion is proposed. 2. Through analysis and research, it is found that remote sensing image classification can be improved from two aspects: on the one hand, improved feature; on the other hand, Therefore, this paper proposes an improved algorithm based on these two aspects. The improved method is based on the existing features, including texture features, histogram features, dimensionality reduction features and the advantages of all kinds of features. The above three kinds of features are normalized fused to obtain the fusion features. Then the hyperspectral remote sensing data single feature and fusion feature are classified by using Real boost Gentle boost Modest AdaBoost, three classification algorithms of AdaBoost ensemble. The improved method based on RVM algorithm is to improve the local binary contrast algorithm and Morphologic algorithm to obtain L-M Algorithm algorithm, and then embed the improved algorithm into RVM algorithm. Then the fusion feature classification results are obtained. In order to make the experiment more fully, this paper also uses SVM and AdaBoost algorithm to carry on the classification contrast experiment .3. the classification algorithm choice. The performance of the classification algorithm directly determines the classification effect, therefore, The selection of classification algorithm is a key step in hyperspectral image classification. Experimental results show that, compared with the accuracy of single feature classification, Therefore, the fusion algorithm proposed in this paper is more reliable and accurate for feature classification.
【學位授予單位】：北方民族大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP751

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本文編號：1610132