本文選題：SAR圖像 + 目標(biāo)檢測(cè)　； 參考：《西安電子科技大學(xué)》2014年碩士論文

【摘要】：隨著合成孔徑雷達(dá)成像技術(shù)與應(yīng)用領(lǐng)域的快速發(fā)展，合成孔徑雷達(dá)的信息采集能力不斷增強(qiáng)，人工解譯已較難適應(yīng)其數(shù)據(jù)量的快速增長，借助于計(jì)算機(jī)、機(jī)器學(xué)習(xí)和模式識(shí)別相關(guān)技術(shù)對(duì)合成孔徑雷達(dá)圖像進(jìn)行自動(dòng)或半自動(dòng)地解譯可以在較大程度上提高對(duì)數(shù)據(jù)的處理效率，這對(duì)于軍事和民用領(lǐng)域都具有良好的應(yīng)用價(jià)值。隨著SAR圖像分辨率的提高，它在軍事領(lǐng)域中的應(yīng)用也由最初的檢測(cè)目標(biāo)逐步擴(kuò)展為檢測(cè)并識(shí)別目標(biāo)，基于高分辨SAR圖像的機(jī)動(dòng)目標(biāo)檢測(cè)、鑒別、識(shí)別是SAR圖像解譯領(lǐng)域中重要的研究方向，，已成為國內(nèi)外的研究熱點(diǎn)。本文從高分辨SAR圖像中機(jī)動(dòng)目標(biāo)的檢測(cè)、鑒別與識(shí)別三個(gè)方面進(jìn)行研究，從分類的角度考慮上述三個(gè)問題，所做主要工作如下： (1)研究了經(jīng)典的恒虛警率檢測(cè)方法，分析了SAR圖像中機(jī)動(dòng)目標(biāo)與常見背景區(qū)域的統(tǒng)計(jì)特性。針對(duì)SAR圖像分辨率的提高，提出了對(duì)目標(biāo)和背景同時(shí)建模的基于貝葉斯分類器的機(jī)動(dòng)目標(biāo)檢測(cè)算法。此外，由于在SAR圖像的目標(biāo)檢測(cè)階段所要處理的數(shù)據(jù)量是巨大的，因此，在上述算法的基礎(chǔ)上進(jìn)一步提出用視覺顯著注意模型先求取圖像中的顯著區(qū)域，再進(jìn)一步用檢測(cè)方法進(jìn)行檢測(cè)，算法不僅具有較好的檢測(cè)性能，更有良好的實(shí)時(shí)性。 (2)研究了表征學(xué)習(xí)的基本理論方法，并將表征學(xué)習(xí)應(yīng)用于高分辨SAR圖像的機(jī)動(dòng)目標(biāo)特征提取上。在此基礎(chǔ)上分別提出了基于稀疏表示的高分辨SAR圖像機(jī)動(dòng)目標(biāo)鑒別方法和基于One-Class SVM的高分辨SAR圖像機(jī)動(dòng)目標(biāo)鑒別方法�；谙∈璞硎镜哪繕�(biāo)鑒別在正樣本較少的情況下也獲得了較好的鑒別性能，而基于One-Class SVM的目標(biāo)鑒別在正樣本未知的條件下，也具有良好的鑒別性能，克服了實(shí)際中目標(biāo)訓(xùn)練樣本未知的鑒別難題。 (3)研究了在語義分析中常用的詞袋（BoW）模型的基本思想及其典型應(yīng)用，分析了高分辨SAR圖像中機(jī)動(dòng)目標(biāo)的常用特征，提出了一種基于BoW模型的高分辨SAR圖像機(jī)動(dòng)目標(biāo)識(shí)別方法。并通過實(shí)驗(yàn)比較了該方法與稀疏表示分類器的在高分辨SAR圖像機(jī)動(dòng)目標(biāo)識(shí)別中的性能。 本文的工作得到了國家重點(diǎn)基礎(chǔ)研究發(fā)展計(jì)劃（973計(jì)劃）： No.2013CB329402，國家自然科學(xué)基金（61072108，60601029，60971112，61173090），新世紀(jì)優(yōu)秀人才項(xiàng)目：NCET-10-0668，高等學(xué)校學(xué)科創(chuàng)新引智計(jì)劃（111計(jì)劃）：No. B0704，教育部博士點(diǎn)基金(20120203110005)，武器裝備預(yù)研基金項(xiàng)目(9*****)，以及華為創(chuàng)新研究計(jì)劃項(xiàng)目(IRP-2013-01-09)的資助。
[Abstract]:With the rapid development of synthetic Aperture Radar (SAR) imaging technology and applications, the ability of SAR information acquisition has been enhanced, and it is difficult to adapt to the rapid growth of data volume by manual interpretation. The automatic or semi-automatic interpretation of SAR images by machine learning and pattern recognition techniques can improve the efficiency of data processing to a large extent, which has good application value in both military and civilian fields. With the improvement of SAR image resolution, the application of SAR image in the military field is gradually expanded from the original detection target to detect and recognize the target, and the maneuvering target detection and identification based on high-resolution SAR image. Recognition is an important research direction in the field of SAR image interpretation, and has become a hot spot at home and abroad. In this paper, the detection, identification and recognition of maneuvering targets in high-resolution SAR images are studied. The above three problems are considered from the perspective of classification. The main work is as follows: (1) the classical CFAR detection method is studied. The statistical characteristics of maneuvering targets and common background regions in SAR images are analyzed. Aiming at the improvement of SAR image resolution, a maneuvering target detection algorithm based on Bayesian classifier is proposed. In addition, since the amount of data to be processed in the target detection phase of SAR images is huge, a visual salient attention model is proposed to obtain the salient regions of the SAR images first based on the above algorithms. The algorithm not only has good detection performance, but also has good real-time performance. (2) the basic theoretical method of representation learning is studied. Representation learning is applied to feature extraction of maneuvering targets in high resolution SAR images. On the basis of the above, the maneuvering target identification methods based on sparse representation and One-Class SVM are proposed, respectively. The target discriminant based on sparse representation also obtains better discriminant performance under the condition of fewer positive samples, while the target discriminant based on One-Class SVM also has good discriminant performance under the condition of unknown positive samples. It overcomes the problem of unknown target training samples in practice. (3) the basic ideas and typical applications of the word bag (BoW) model commonly used in semantic analysis are studied, and the common features of maneuvering targets in high-resolution SAR images are analyzed. A maneuvering target recognition method for high resolution SAR images based on Bow model is proposed. The performance of this method is compared with that of sparse representation classifier in high resolution SAR image maneuvering target recognition. The work of this paper has been obtained from the National key basic Research and Development Program (973 Program): No. 2013CB329402, the National Natural Science Foundation of China (61072108), the New Century Outstanding Talent Project: NCET-10-0668, the discipline Innovation and introduction Program of higher Education (111 Program): no. B0704, Ministry of Education Ph.D. Foundation (20120203110005), weapons and equipment Pre-Research Foundation (9 *), and Huawei Innovation Research Program (IRP-2013-01-09).
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN957.52

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本文編號(hào)：2086415