本文選題：SAR圖像 + 目標(biāo)識別�。� 參考：《哈爾濱理工大學(xué)》2014年碩士論文

【摘要】：合成孔徑雷達(dá)(Synthetic Aperture Radar, SAR)圖像自動目標(biāo)識別(AutomaticTarget Recognition, ATR)技術(shù)是在無人為干涉的情況下，，利用采集到的SAR圖像數(shù)據(jù)，準(zhǔn)確的找到感興趣的區(qū)域，并提取目標(biāo)的特征信息，識別出目標(biāo)的類別屬性。在SARATR體系中包含最主要的三個步驟，即SAR圖像預(yù)處理，SAR圖像目標(biāo)特征提取和SAR圖像目標(biāo)分類識別。本論文針對這三個步驟，完成了以下任務(wù)： 1. SAR圖像的預(yù)處理方法：本論文研究了一種基于復(fù)Contourlet變換和隱馬爾科夫樹(HMT)模型的SAR圖像去除相干斑噪聲方法。該方法利用復(fù)Contourlet變換的多尺度、多方向性和平移不變性的特點(diǎn)，將其與HMT模型相結(jié)合，從而能夠準(zhǔn)確地描述復(fù)Contourlet變換域系數(shù)在相鄰尺度間的相關(guān)性。對實(shí)驗(yàn)結(jié)果的定量分析可知，該方法取得了良好的去噪效果。 2. SAR圖像的特征提取方法：流形學(xué)習(xí)方法是模式識別的基本方法，本文應(yīng)用的最大異類距離嵌入特征提取(Maximum Interclass Distance Embedding, MIDE)方法的創(chuàng)新性體現(xiàn)在既結(jié)合了主成份分析(Personal Computer Assistant, PCA)的差異特性和局部保持映射(Local Preserving Projection, LPP)的鄰域信息，并同時融入了數(shù)據(jù)集的類別信息。利用此方法旨在找尋一個線性嵌入的映射，能夠?qū)⑼队昂螳@得的不同類別的SAR圖像特征彼此互相遠(yuǎn)離。 3. SAR圖像目標(biāo)識別算法：文中提出了一種基于神經(jīng)網(wǎng)絡(luò)集成模型的SAR圖像分類識別算法。該方法很好的克服了SAR圖像對方位角敏感的問題，針對同向目標(biāo)的特征空間訓(xùn)練一個神經(jīng)網(wǎng)絡(luò)實(shí)現(xiàn)目標(biāo)分類，并使用另一個二級神經(jīng)網(wǎng)絡(luò)對多個單向目標(biāo)識別器的識別結(jié)果進(jìn)行結(jié)合，提高識別精度。 將SAR圖像自動目標(biāo)識別三個階段的三種方法同時應(yīng)用在SAR圖像自動目標(biāo)識別中，通過仿真實(shí)驗(yàn)，定量分析可知，其最終的目標(biāo)識別率高于其它較為優(yōu)秀的算法所達(dá)到的目標(biāo)識別率，證明該論文提出的算法是切實(shí)可行的。
[Abstract]:The automatic Target recognition (ATR) technology of synthetic Aperture radar (SAR) image is to accurately locate the region of interest and extract the feature information of the target by using the collected SAR image data under the condition of no one interference. Identifies the target's category attributes. There are three main steps in SARATR system, that is, SAR image preprocessing, SAR image feature extraction and SAR image target classification and recognition. According to these three steps, this thesis has completed the following tasks: 1. The method of SAR image preprocessing: in this paper, a speckle noise removal method for SAR image based on complex Contourlet transform and Hidden Markov Tree (HMT) model is studied. This method combines the multi-scale, multi-directivity and translation invariance of complex Contourlet transform with HMT model, so it can accurately describe the correlation between adjacent scales of complex Contourlet transform domain coefficients. The quantitative analysis of the experimental results shows that the method has achieved a good denoising effect. 2. The feature extraction method of SAR image: manifold learning method is the basic method of pattern recognition. The innovation of Maximum Interclass distance embedding (MIDE) method used in this paper lies in the combination of the difference characteristics of personal computer Assistance (PCA) and the neighborhood information of Local preserving Project (LPP). At the same time, it incorporates the category information of the data set. Using this method, we can find a linear embedded map, which can separate different kinds of SAR image features from each other. Target recognition algorithm for SAR images: a classification and recognition algorithm for SAR images based on neural network ensemble model is proposed in this paper. This method can overcome the problem that SAR image is sensitive to azimuth, and train a neural network to classify the target in the feature space of the same target. Another two-level neural network is used to combine the recognition results of multiple unidirectional target recognizers to improve the recognition accuracy. The three methods of automatic target recognition in SAR image are applied to the automatic target recognition of SAR image at the same time. The final target recognition rate is higher than that achieved by other better algorithms, which proves that the algorithm proposed in this paper is feasible.
【學(xué)位授予單位】：哈爾濱理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN957.52

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本文編號：2059616