基于稀疏表示的SAR目標識別算法研究
發(fā)布時間：2018-08-22 07:42
【摘要】：合成孔徑雷達(Synthetic Aperture Radar,SAR)是一種具有高分辨力的成像雷達。由于全天時、全天候的成像優(yōu)勢,它在民用和軍用領(lǐng)域被廣泛應(yīng)用。作為應(yīng)用之一的SAR目標識別由于對國防預(yù)警的重要意義成為廣大學(xué)者研究的熱點之一。稀疏表示從過完備字典中選取盡量少的原子線性重構(gòu)信號,應(yīng)用于識別問題時,不僅有天然的識別信息包含在稀疏表示系數(shù)中還表現(xiàn)出優(yōu)良的抗噪性,而SAR目標識別的一個難題就是斑點噪聲,因此以稀疏表示理論為基礎(chǔ)的SAR目標識別具有廣闊的研究前景。本文以稀疏表示為基礎(chǔ),結(jié)合SAR圖像的特點,在SAR圖像預(yù)處理、特征提取和目標識別方面展開研究,主要的研究內(nèi)容如下:1.結(jié)合MSTAR數(shù)據(jù)庫的SAR目標圖像特點,研究了基于支持向量機(Support Vector Machine,SVM)分類思想的SAR圖像預(yù)處理方法,經(jīng)過對數(shù)變換、基于SVM的穩(wěn)定SAR圖像分割、后處理的流程后得到的SAR圖像既保留了目標的細節(jié)信息又大大減弱了斑點噪聲的影響,為后續(xù)的識別提供了更清晰的SAR目標圖像。2.針對稀疏鄰域保留嵌入(Sparse Neighborhood Preserving Embedding,SNPE)應(yīng)用于SAR目標識別時在稀疏表示模型上的不足,提出改進的最大化稀疏重構(gòu)間隙投影(Maximize Sparse Reconstruction Margin Projections,MSRMP),新算法不僅提升了識別率,還表現(xiàn)出對分類策略的不敏感性,只要特征維數(shù)足夠大時,在不同分類器上識別率都能保持穩(wěn)定,而且新算法傳承了稀疏表示的抗噪性,在噪聲嚴重的數(shù)據(jù)上仍保持較高的識別率。3.針對單個SAR目標擁有多角度圖像的情況,對聯(lián)合稀疏表示(Joint Sparse Representation,JSR)模型探討,提出改進的聯(lián)合稀疏表示(Improved Joint Sparse Representation,IJSR)模型,通過1范數(shù)最小化和低秩矩陣恢復(fù)措施尋求同一SAR目標多角度圖像的共有模式,利用共有模式提取信息實現(xiàn)分類識別,將改進的聯(lián)合稀疏表示和聯(lián)合稀疏表示分別應(yīng)用于MSTAR數(shù)據(jù)庫上,結(jié)合稀疏表示分類策略的實驗顯示改進的聯(lián)合稀疏表示提高了識別率。
[Abstract]:Synthetic Aperture Radar (Synthetic Aperture) is an imaging radar with high resolution. It is widely used in civil and military fields because of the advantage of all-weather and all-weather imaging. As one of the applications, SAR target recognition has become one of the hot research topics for many scholars because of its importance to national defense early warning. Sparse representation selects as few atomic linear reconstructed signals as possible from overcomplete dictionaries. When applied to recognition problems, not only natural recognition information is included in sparse representation coefficients, but also excellent noise resistance is shown. The speckle noise is a difficult problem in SAR target recognition, so the SAR target recognition based on sparse representation theory has a broad research prospect. Based on sparse representation and combined with the characteristics of SAR images, this paper researches on SAR image preprocessing, feature extraction and target recognition. The main research contents are as follows: 1. According to the characteristics of SAR target image in MSTAR database, a SAR image preprocessing method based on SVM (Support Vector Machine (SVM) classification idea is studied. After logarithmic transformation, stable SAR image segmentation based on SVM is achieved. The SAR image obtained by the post-processing process not only retains the detailed information of the target but also greatly reduces the influence of speckle noise, which provides a clearer SAR target image .2for the subsequent recognition. Aiming at the shortage of sparse representation model of sparse neighborhood reserved embedded (Sparse Neighborhood Preserving (SNPE) in SAR target recognition, an improved maximum sparse reconstruction gap projection (Maximize Sparse Reconstruction Margin projects (MSRMP) is proposed. The new algorithm not only improves the recognition rate, but also improves the performance of the algorithm. It also shows insensitivity to classification strategy. As long as the feature dimension is large enough, the recognition rate on different classifiers is stable, and the new algorithm inherits the anti-noise property of sparse representation. Still maintain a high recognition rate of. 3 on noisy data. For a single SAR target with multi-angle images, this paper discusses the joint sparse representation (Joint Sparse representation (JSR) model, and proposes an improved joint sparse representation (Improved Joint Sparse (JSR) model. The common pattern of multi-angle image of the same SAR target is obtained by minimizing 1-norm and restoring low rank matrix, and the common pattern information is extracted to realize classification and recognition. The improved joint sparse representation and the joint sparse representation are applied to the MSTAR database respectively. The experimental results with the sparse representation classification strategy show that the improved joint sparse representation improves the recognition rate.
【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
，
本文編號：2196448
[Abstract]:Synthetic Aperture Radar (Synthetic Aperture) is an imaging radar with high resolution. It is widely used in civil and military fields because of the advantage of all-weather and all-weather imaging. As one of the applications, SAR target recognition has become one of the hot research topics for many scholars because of its importance to national defense early warning. Sparse representation selects as few atomic linear reconstructed signals as possible from overcomplete dictionaries. When applied to recognition problems, not only natural recognition information is included in sparse representation coefficients, but also excellent noise resistance is shown. The speckle noise is a difficult problem in SAR target recognition, so the SAR target recognition based on sparse representation theory has a broad research prospect. Based on sparse representation and combined with the characteristics of SAR images, this paper researches on SAR image preprocessing, feature extraction and target recognition. The main research contents are as follows: 1. According to the characteristics of SAR target image in MSTAR database, a SAR image preprocessing method based on SVM (Support Vector Machine (SVM) classification idea is studied. After logarithmic transformation, stable SAR image segmentation based on SVM is achieved. The SAR image obtained by the post-processing process not only retains the detailed information of the target but also greatly reduces the influence of speckle noise, which provides a clearer SAR target image .2for the subsequent recognition. Aiming at the shortage of sparse representation model of sparse neighborhood reserved embedded (Sparse Neighborhood Preserving (SNPE) in SAR target recognition, an improved maximum sparse reconstruction gap projection (Maximize Sparse Reconstruction Margin projects (MSRMP) is proposed. The new algorithm not only improves the recognition rate, but also improves the performance of the algorithm. It also shows insensitivity to classification strategy. As long as the feature dimension is large enough, the recognition rate on different classifiers is stable, and the new algorithm inherits the anti-noise property of sparse representation. Still maintain a high recognition rate of. 3 on noisy data. For a single SAR target with multi-angle images, this paper discusses the joint sparse representation (Joint Sparse representation (JSR) model, and proposes an improved joint sparse representation (Improved Joint Sparse (JSR) model. The common pattern of multi-angle image of the same SAR target is obtained by minimizing 1-norm and restoring low rank matrix, and the common pattern information is extracted to realize classification and recognition. The improved joint sparse representation and the joint sparse representation are applied to the MSTAR database respectively. The experimental results with the sparse representation classification strategy show that the improved joint sparse representation improves the recognition rate.
【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
，
本文編號：2196448
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