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  本文選題：SAR目標(biāo)識別 + 稀疏表示　； 參考：《電子科技大學(xué)》2014年碩士論文

【摘要】：SAR圖像目標(biāo)識別作為信息獲取的關(guān)鍵技術(shù),具有重要的應(yīng)用價值,一直是國內(nèi)外目標(biāo)識別領(lǐng)域的研究熱點。近年來稀疏表示理論被廣泛應(yīng)用于各類圖像處理領(lǐng)域,并且在人臉識別中已取得了良好的效果。本文著重研究了將稀疏表示理論應(yīng)用于SAR圖像目標(biāo)識別中的兩個關(guān)鍵步驟:冗余字典的構(gòu)造和稀疏系數(shù)的求解。主要研究內(nèi)容如下:(1)針對原始冗余字典類別差異性不足和規(guī)模較大的兩個缺陷,利用冗余字典的二維結(jié)構(gòu)提出了字典的縱、橫雙向改進(jìn)方法。在縱向改進(jìn)中,針對SAR圖像由確定信息和不確定信息組成的特點,利用小波變換來提取有利于識別的低頻確定信息,并經(jīng)過2DPCA降維處理得到小波域字典。在橫向改進(jìn)中,利用K-近鄰算法的樣本選擇思想,實現(xiàn)了字典原子的橫向動態(tài)篩選,從而生成基于近鄰子空間的動態(tài)字典。(2)在完成了冗余字典構(gòu)造的基礎(chǔ)上,對稀疏系數(shù)分解算法進(jìn)行研究。將最小L1范數(shù)凸優(yōu)化算法和OMP算法進(jìn)行對比分析,驗證了后者的類別差異性和分解效率均優(yōu)于前者。同時,針對OMP算法稀疏度K未知的問題,提出了用類別統(tǒng)計量C來替換稀疏度K作為算法迭代終止條件的改進(jìn)方法,并通過仿真實驗驗證了改進(jìn)后的OMP算法具有更好的識別效果。(3)根據(jù)稀疏分解系數(shù)的分布特點,總結(jié)出最大系數(shù)準(zhǔn)則、歸類系數(shù)最大準(zhǔn)則兩種分類判別準(zhǔn)則,并對這兩種準(zhǔn)則進(jìn)行仿真對比。仿真結(jié)果表明,歸類系數(shù)最大準(zhǔn)則能夠取得更高的識別率,故本文利用它來完成分類識別器的設(shè)計。(4)基于MSTAR數(shù)據(jù)庫,統(tǒng)計了本文識別算法在各種非理想情況下的識別率,驗證了在含有噪聲、遮擋及分辨率下降情況的算法魯棒性。
[Abstract]:As a key technology of information acquisition, SAR image target recognition has important application value and has been a hot research topic in the field of target recognition at home and abroad. In recent years, sparse representation theory has been widely used in various image processing fields, and has achieved good results in face recognition. This paper focuses on two key steps of applying sparse representation theory to target recognition in SAR images: the construction of redundant dictionaries and the solution of sparse coefficients. The main research contents are as follows: (1) aiming at the deficiency of the difference of the original redundant dictionaries and the two defects of large scale, this paper puts forward the vertical and horizontal bidirectional improvement methods of the redundant dictionaries by using the two-dimensional structure of the redundant dictionaries. In the longitudinal improvement, according to the characteristic that SAR image is composed of determinate information and uncertain information, wavelet transform is used to extract the low-frequency deterministic information which is favorable to recognition, and the dictionary in wavelet domain is obtained by 2DPCA dimensionality reduction. In the lateral improvement, by using the idea of sample selection of K- nearest neighbor algorithm, we realize the horizontal dynamic selection of dictionary atoms, so as to generate a dynamic dictionary based on nearest neighbor subspace. (2) based on the construction of redundant dictionary, the redundant dictionary is constructed. The sparse coefficient decomposition algorithm is studied. By comparing the minimum L1 norm convex optimization algorithm with the OMP algorithm, it is verified that the class difference and decomposition efficiency of the latter are better than the former. At the same time, aiming at the problem that the sparsity K of OMP algorithm is unknown, an improved method is proposed to replace the sparse degree K with class statistic C as the iterative termination condition of the algorithm. The simulation results show that the improved OMP algorithm has better recognition effect. According to the distribution characteristics of sparse decomposition coefficient, the maximum coefficient criterion and the maximum classification coefficient criterion are summarized. The two criteria are simulated and compared. The simulation results show that the maximum criterion of classification coefficient can achieve a higher recognition rate, so this paper uses it to complete the design of the classifier. (4) based on the MSTAR database, the recognition rate of the algorithm in this paper is calculated under various non-ideal conditions. The robustness of the algorithm with noise, occlusion and resolution reduction is verified.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN957.52

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

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2 武妍;夏瑩;;一種基于完全2DPCA的二次特征選擇方法[J];計算機(jī)工程;2008年03期

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本文編號：1777872