本文選題：SAR圖像 + 局部特征�。� 參考：《國(guó)防科學(xué)技術(shù)大學(xué)》2016年博士論文

【摘要】：SAR圖像特征提取是SAR圖像信息提取與解譯的基礎(chǔ)工作,良好的特征能夠完備地表征圖像或者目標(biāo)的核心信息,有利于后續(xù)的識(shí)別分類工作的開展,因而受到了國(guó)內(nèi)外研究人員的關(guān)注,成為SAR圖像解譯的理論研究熱點(diǎn)之一。在SAR圖像解譯的實(shí)際應(yīng)用中,圖像和目標(biāo)的全局特征提取通常是非魯棒的,存在一定隨機(jī)變化和模糊。SAR圖像局部特征提取研究是解決上述問(wèn)題的一種思路,是對(duì)特征提取技術(shù)的進(jìn)一步發(fā)展,也是SAR圖像計(jì)算機(jī)解譯技術(shù)的重要信息支撐。而目前SAR圖像局部特征提取普遍沿用光學(xué)圖像處理的方法,存在一定的不相適應(yīng)的情況,因此根據(jù)SAR圖像成像特性與噪聲特性來(lái)研究其局部特征提取算法很有必要。通過(guò)研究與完善SAR圖像局部特征理論與提取算法,克服兩個(gè)不足:(1)傳統(tǒng)的全局特征提取方法魯棒性差;(2)光學(xué)圖像局部特征提取方法對(duì)于SAR圖像的適應(yīng)性差,從而提高基于局部特征的SAR圖像匹配與目標(biāo)檢測(cè)識(shí)別的精度與效率。本文圍繞SAR圖像局部特征提取的這一主線,從SAR圖像目標(biāo)的顯著性特征、目標(biāo)的散射中心點(diǎn)集特征和SAR圖像局部不變特征三個(gè)方向入手,采用理論分析和實(shí)驗(yàn)驗(yàn)證相結(jié)合的研究方法,深入研究探討:(1)SAR圖像目標(biāo)顯著性區(qū)域、顯著性特征提取與應(yīng)用;(2)目標(biāo)散射中心點(diǎn)集特征序貫匹配識(shí)別SAR圖像車輛目標(biāo);(3)SAR圖像局部不變特征提取新方法與應(yīng)用。作為整個(gè)論文的理論基礎(chǔ),第二章主要論述了圖像局部不變特征理論及典型的方法,SAR圖像的成像概述、噪聲特性和幾種基本特征,針對(duì)本文研究重點(diǎn),對(duì)SAR圖像局部不變特征的研究基礎(chǔ)、發(fā)展現(xiàn)狀和主要方法做了闡述和總結(jié)。第三章在SAR圖像噪聲特性分析基礎(chǔ)上,結(jié)合視覺(jué)顯著性理論,根據(jù)SAR圖像局部復(fù)雜度和自差異測(cè)度設(shè)計(jì)了一種SAR圖像的顯著性區(qū)域檢測(cè)方法,與經(jīng)典的視覺(jué)顯著性算法進(jìn)行顯著性區(qū)域檢測(cè)實(shí)驗(yàn)對(duì)比,以及與經(jīng)典的CFAR方法進(jìn)行SAR圖像目標(biāo)檢測(cè)實(shí)驗(yàn)對(duì)比,均取得了良好的實(shí)驗(yàn)結(jié)果,表明該方法勝任在SAR圖像上提取顯著性區(qū)域,檢測(cè)高價(jià)值地物目標(biāo),具有很好的應(yīng)用性。第四章結(jié)合模式識(shí)別理論中的點(diǎn)模式匹配方法和SAR圖像屬性散射中心模型理論,提出了一種基于目標(biāo)散射中心點(diǎn)集特征的序貫匹配方法。首先基于屬性散射中心模型提取SAR圖像域目標(biāo)散射中心的特征,使用該散射中心點(diǎn)集位置分量和表征散射中心幾何結(jié)構(gòu)信息的屬性散射中心特征頻率影響因子作為匹配特征,依次序貫匹配,實(shí)現(xiàn)車輛目標(biāo)的對(duì)比識(shí)別。實(shí)驗(yàn)結(jié)果與國(guó)外同類方法相比,準(zhǔn)確性和識(shí)別率都顯示出優(yōu)勢(shì)。第五章提出了一種SAR圖像局部特征提取新方法。首先分析了SAR圖像的像素梯度信息提取方法和常見的梯度算子,例如ROA算子、ROEWA算子和GR算子。然后介紹了Harris算子、Lo G算子及其適應(yīng)SAR圖像特性的改進(jìn)—多尺度Harris算子。接下來(lái)介紹了圖像局部二值模式特征和旋轉(zhuǎn)不變局部二值模式特征,多尺度局部梯度比率直方圖特征MLGRPH,并且實(shí)驗(yàn)驗(yàn)證了該特征在SAR圖像上的旋轉(zhuǎn)不變性能。在上述研究基礎(chǔ)上,提出了基于MLGRPH特征的SAR圖像局部不變特征提取新方法。實(shí)驗(yàn)驗(yàn)證環(huán)節(jié),采用不同時(shí)相、不同波段、不同極化方式和不同視角成像的多組SAR數(shù)據(jù)對(duì)經(jīng)典SIFT方法、SIFT-OCT方法和該新方法開展了實(shí)驗(yàn)分析與對(duì)比,結(jié)果表明該方法在性能上優(yōu)于SIFT方法、SIFT-OCT方法,還可以進(jìn)一步性能提升的潛力。第六章對(duì)本文的工作總結(jié)歸納,并對(duì)SAR圖像局部特征提取方法的下一步研究工作進(jìn)行了展望。
[Abstract]:The feature extraction of SAR image is the basic work of information extraction and interpretation of SAR images. Good features can fully characterize the core information of images or targets, which is beneficial to the follow-up recognition and classification work. Therefore, it has attracted the attention of researchers both at home and abroad. It has become one of the hot topics in the theoretical research of SAR image interpretation. The interpretation of SAR images is interpreted. In practical application, the global feature extraction of image and target is usually non robust. There is a certain random change and local feature extraction of fuzzy.SAR image. It is a way to solve the above problems. It is the further development of the feature extraction technology and an important information support for the SAR image computing machine interpretation technology. And the current SAR diagram It is necessary to study the local feature extraction algorithm based on the image characteristics and noise characteristics of SAR image, so it is necessary to study and improve the local feature theory and extraction algorithm of SAR image, so as to overcome two shortcomings: (1) traditional global special. The robustness of the extraction method is poor; (2) the local feature extraction method of the optical image is poor in the adaptability of the SAR image, thus improving the accuracy and efficiency of the SAR image matching based on the local feature and the target detection recognition. This paper focuses on the main line of the local feature extraction of the SAR image, from the saliency feature of the target of the SAR image and the scattering center of the target. Starting with three directions of point set feature and local invariant feature of SAR image, the research method combined with theoretical analysis and experimental verification is used to study and discuss: (1) significant feature extraction and application of SAR image target, (2) the feature sequential matching of target scattering center set recognition for SAR image vehicle target; (3) local SAR image is not local. A new method and application of variable feature extraction. As the theoretical basis of the whole thesis, the second chapter mainly discusses the image local invariant feature theory and typical methods, the overview of the image, the noise characteristics and several basic features of the SAR image. In this paper, the research foundation, the development status and the main methods for the local invariant features of the SAR image are discussed. The third chapter, based on the analysis of SAR image noise characteristics, combined with the visual significance theory, designed a significant regional detection method for SAR images based on the local complexity and self difference measure of SAR images, compared with the classic visual saliency algorithm for significant regional detection experiments, and with the classic CFAR. The method is compared with the SAR image target detection experiment, and good experimental results are obtained. It shows that the method is competent for extracting significant regions on SAR images and detecting high value objects. The fourth chapter combines the point pattern matching method and the SAR image attribute scattering center model theory in the pattern recognition theory. A sequential matching method based on the feature of the target scattering center point set is proposed. Firstly, the feature of the target scattering center of the SAR image domain is extracted based on the attribute scattering center model, and the characteristic frequency influence factor of the attribute scattering center of the scattering center is used as the matching feature. In the fifth chapter, a new method for extracting local features of SAR images is proposed. Firstly, the method of extracting the pixel gradient information from the SAR image and the common gradient operators, such as ROA operator, ROEWA calculation, are analyzed. Then, the Harris operator, the Lo G operator and the improvement of the Lo G operator and the improvement of the SAR image characteristics are introduced. Then the features of the local two value pattern and the rotation invariant local two value pattern, the multi-scale local gradient ratio histogram feature MLGRPH are introduced, and the experiment verifies that the feature is on the SAR image. On the basis of the above research, a new method for extracting local invariant features of SAR images based on MLGRPH features is proposed. Experimental verification links are carried out by using different phases, different bands, different polarization modes and different groups of SAR data from different angles of view. The experimental analysis and analysis of the classical SIFT method, SIFT-OCT method and the new method are carried out. In contrast, the results show that the method is superior to the SIFT method in performance, and the SIFT-OCT method can further improve the potential of performance. The sixth chapter summarizes the work of this paper, and looks forward to the next research work of the local feature extraction method of SAR images.
【學(xué)位授予單位】：國(guó)防科學(xué)技術(shù)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TN957.52

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