本文選題：遙感圖像　切入點(diǎn)：目標(biāo)分級識別　出處：《哈爾濱工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：遙感圖像的目標(biāo)識別是空間遙感技術(shù)的主要應(yīng)用之一。近年來,隨著遙感圖像分辨率的提高,越來越多的目標(biāo)信息能夠從遙感圖像中挖掘出來,為描述目標(biāo)提供了重要的支撐。因此,在遙感圖像應(yīng)用領(lǐng)域,對目標(biāo)進(jìn)行進(jìn)一步地細(xì)致的研究已經(jīng)受到了越來越多的重視。但是,遙感圖像分辨率的提升也為遙感圖像的處理帶來了許多問題,通常來說,傳統(tǒng)的方法一般只停留在對目標(biāo)自身的關(guān)注,很少更深入地對目標(biāo)的細(xì)節(jié)部位進(jìn)行識別,同時(shí),在處理高數(shù)據(jù)量的高分辨率圖像時(shí),傳統(tǒng)利用向量來描述圖像中的特征的方法效率不高。本文主要針對這些問題提出了基于支持張量機(jī)模型的遙感圖像目標(biāo)分級識別方法,通過對關(guān)注的目標(biāo)進(jìn)行由整體到局部的分級識別,更有效地把我目標(biāo)的細(xì)節(jié)信息,并利用特征張量模型描述圖像中的目標(biāo),由于張量能夠保持?jǐn)?shù)據(jù)的空間結(jié)構(gòu),因而能夠取得較好的結(jié)果。本文首先研究了目標(biāo)的局部空間特征提取與特征張量表達(dá)模型的構(gòu)建。遙感圖像自身就可以看作是張量的數(shù)據(jù)形式,因此利用張量可以更直接地描述圖像數(shù)據(jù)的空間坐標(biāo)與光譜信息,同時(shí)結(jié)合對圖像局部空間特征的提取得到目標(biāo)圖像相關(guān)特征信息,進(jìn)行目標(biāo)圖像的特征張量描述。為了更好地保持張量的空間特性,本課題采用了局部空間不變特征與加速魯棒特征兩種局部空間特征來建立特征張量模型,并研究分析了它們的優(yōu)缺點(diǎn)與適用情況。之后,本文對支持張量機(jī)學(xué)習(xí)分類模型的學(xué)習(xí)訓(xùn)練算法與過程進(jìn)行了闡述。利用張量來實(shí)現(xiàn)目標(biāo)的分類識別能夠更好地利用圖像自身的空間結(jié)構(gòu)信息,并在一定程度上減少向量模型中維數(shù)災(zāi)難的發(fā)生。本文通過一般的支持向量機(jī)算法引出支持張量機(jī)的學(xué)習(xí)模型,本課題主要利用基于梯度下降算法的學(xué)習(xí)模型對支持張量機(jī)進(jìn)行了訓(xùn)練,通過對訓(xùn)練樣本特征張量模型與對應(yīng)分類值的反復(fù)迭代,求得最優(yōu)分類超平面,對關(guān)注的目標(biāo)和背景進(jìn)行區(qū)分。在建立好支持張量機(jī)模型之后,通過不同的訓(xùn)練樣本訓(xùn)練得到不同的分類器,進(jìn)行目標(biāo)的分級識別實(shí)驗(yàn)研究。本文通過對飛機(jī)、艦船兩類目標(biāo)以及它們的細(xì)節(jié)部位等進(jìn)行實(shí)驗(yàn)測試,驗(yàn)證了支持張量機(jī)模型在目標(biāo)分級識別應(yīng)用中的準(zhǔn)確性與可靠性。實(shí)驗(yàn)結(jié)果表明,利用支持張量機(jī)模型能夠比較有效地實(shí)現(xiàn)遙感圖像中目標(biāo)的分級識別,方法具備一定的實(shí)際意義與應(yīng)用價(jià)值。
[Abstract]:Object recognition of remote sensing image is one of the main applications of space remote sensing technology. In recent years, with the improvement of remote sensing image resolution, more and more target information can be extracted from remote sensing image. Therefore, in the field of remote sensing image application, more and more attention has been paid to the study of target. The improvement of remote sensing image resolution also brings many problems for remote sensing image processing. Generally speaking, the traditional methods only focus on the target itself, and rarely identify the target's detailed parts more deeply, at the same time, the traditional methods only focus on the target itself, at the same time, When dealing with high resolution images with high data volume, the traditional method of using vectors to describe the features of images is not efficient. In this paper, a classification recognition method for remote sensing images based on Zhang Liang model is proposed in order to solve these problems. By classifying the objects of concern from the whole to the local level, we can more effectively describe the details of our targets, and use the feature Zhang Liang model to describe the targets in the image, because Zhang Liang can maintain the spatial structure of the data. Therefore, better results can be obtained. Firstly, this paper studies the local spatial feature extraction of the target and the construction of the Zhang Liang expression model. The remote sensing image itself can be regarded as the data form of Zhang Liang. Therefore, Zhang Liang can more directly describe the spatial coordinate and spectral information of the image data, and combine with the extraction of the local spatial features of the image to obtain the relevant feature information of the target image. In order to better maintain the spatial characteristics of Zhang Liang, two local spatial features, local spatial invariant feature and accelerated robust feature, are adopted to establish the feature Zhang Liang model. After studying and analyzing their advantages and disadvantages and their application. In this paper, the algorithm and process of learning and training supporting Zhang Liang's machine learning classification model are expounded. The spatial structure information of image itself can be better utilized by using Zhang Liang to realize target classification and recognition. And to some extent reduce the occurrence of dimensionality disaster in the vector model. In this paper, the general support vector machine algorithm is used to derive the learning model of support Zhang Liang machine. In this paper, we mainly use the learning model based on gradient descent algorithm to train Zhang Liang machine, and get the optimal classification hyperplane by iterating the training sample feature Zhang Liang model and the corresponding classification value. After establishing the model of supporting Zhang Liang machine, different classifiers are obtained by training different training samples, and the classification recognition experiment of target is carried out. The accuracy and reliability of supporting Zhang Liang machine model in target classification recognition are verified by experimental tests on two kinds of ship targets and their detailed positions. The experimental results show that, The classification recognition of objects in remote sensing images can be realized effectively by using the support Zhang Liang machine model. The method has certain practical significance and application value.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP751

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2 周蓉;支持張量機(jī)的在線學(xué)習(xí)算法研究[D];華南理工大學(xué);2014年

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本文編號：1573212