【摘要】：人造目標(biāo)檢測(cè)是遙感數(shù)據(jù)應(yīng)用的重要環(huán)節(jié),是災(zāi)害救援、軍事偵察等應(yīng)用的基礎(chǔ),檢測(cè)性能與速度的優(yōu)劣直接影響到后續(xù)的實(shí)際應(yīng)用。眾多遙感數(shù)據(jù)中,PolSAR為主動(dòng)成像,具有全天時(shí)全天候的特點(diǎn),這在目標(biāo)實(shí)時(shí)檢測(cè)應(yīng)用中具有極大的優(yōu)勢(shì)。目前PolSAR圖像的獲取能力獲得了極大的提升,圖像覆蓋區(qū)域以及數(shù)據(jù)量越來(lái)越大,這為PolSAR圖像目標(biāo)檢測(cè)應(yīng)用提供了數(shù)據(jù)基礎(chǔ)。然而,傳統(tǒng)算法大多關(guān)注精度指標(biāo),當(dāng)圖像數(shù)據(jù)量過(guò)大時(shí),檢測(cè)處理耗時(shí)也隨之大幅增加。而以人造目標(biāo)檢測(cè)為基礎(chǔ)的諸多場(chǎng)合中,則需要在有限的時(shí)間內(nèi)完成相關(guān)工作,這對(duì)大場(chǎng)景PolSAR圖像人造目標(biāo)檢測(cè)提出了處理速度上的要求。如何在精度滿足要求的條件下,高效完成大場(chǎng)景PolSAR圖像的人造目標(biāo)檢測(cè)成為研究面臨的主要問(wèn)題。本文以大場(chǎng)景PolSAR圖像人造目標(biāo)檢測(cè)為主要研究?jī)?nèi)容,首先從PolSAR圖像表征形式與人造目標(biāo)特性分析入手,明確了人造目標(biāo)在PolSAR圖像中的具體特征,在此基礎(chǔ)上進(jìn)行基本檢測(cè)理論的研究。隨后對(duì)傳統(tǒng)的目標(biāo)提取算法進(jìn)行了加速優(yōu)化,并設(shè)計(jì)了基于快速Wishart計(jì)算的檢測(cè)算法。然后進(jìn)行了圖像均勻度描述因子特征的設(shè)計(jì),該特征具有目標(biāo)區(qū)域提取的能力,且提取速度與傳統(tǒng)紋理特征相比獲得了較大的提高。利用圖像均勻度描述因子特征,設(shè)計(jì)了三種基于該特征的極化目標(biāo)檢測(cè)算法。根據(jù)圖像信息使用量的不同,三種算法具有不同檢測(cè)性能以及檢測(cè)速度,以適應(yīng)不同的精度以及效率需求。最后選擇實(shí)驗(yàn)數(shù)據(jù),分別進(jìn)行陸地環(huán)境人造目標(biāo)檢測(cè)以及海洋艦船目標(biāo)檢測(cè)實(shí)驗(yàn),并從目標(biāo)檢測(cè)精度、重點(diǎn)目標(biāo)檢測(cè)情況以及處理時(shí)間三個(gè)方面對(duì)算法進(jìn)行評(píng)價(jià)。實(shí)驗(yàn)結(jié)果表明,本文設(shè)計(jì)的四種檢測(cè)算法均具有大場(chǎng)景PolSAR圖像人造目標(biāo)檢測(cè)能力。其中,基于快速Wishart計(jì)算的檢測(cè)算法其性能與監(jiān)督信息選取相關(guān),具有較大的性能提升潛力。基于圖像均勻度描述因子特征的三種檢測(cè)算法,能夠在較短的時(shí)間內(nèi)完成人造目標(biāo)檢測(cè)任務(wù),且檢測(cè)精度比傳統(tǒng)方法更高。三種檢測(cè)算法對(duì)圖像信息的使用量不同,其檢測(cè)性能與處理速度具有較大的差異,能夠適應(yīng)不同的精度與效率要求。
[Abstract]:Artificial target detection is an important part of remote sensing data application, and is the basis of disaster rescue, military reconnaissance and other applications. The performance and speed of detection directly affect the subsequent practical applications. Among the many remote sensing data, PolSAR is active imaging, which has the characteristics of all-day, all-weather, which has a great advantage in the application of target real-time detection. At present, the ability of PolSAR image acquisition has been greatly improved, the area covered by the image and the amount of data are more and more large, which provides a data basis for the application of PolSAR image target detection. However, most of the traditional algorithms focus on the accuracy index, when the amount of image data is too large, the detection processing time is also greatly increased. In many occasions based on artificial target detection, it is necessary to complete the related work in a limited time, which puts forward the processing speed requirements for large scene PolSAR image artificial target detection. How to efficiently complete the artificial target detection of large scene PolSAR images under the condition of satisfying the requirement of precision has become the main problem in the research. In this paper, the detection of artificial targets in large scene PolSAR images is the main research content. Firstly, the characteristics of artificial targets in PolSAR images are defined by analyzing the representation form of PolSAR images and the characteristics of artificial targets. On this basis, the basic detection theory is studied. Then the traditional target extraction algorithm is optimized and the detection algorithm based on fast Wishart computation is designed. Then, the feature of image uniformity description factor is designed. The feature has the ability to extract the target region, and the speed of extraction is improved compared with the traditional texture feature. Three polarimetric target detection algorithms based on the feature of image uniformity description factor are designed. According to the different amount of image information, the three algorithms have different detection performance and detection speed to meet different accuracy and efficiency requirements. Finally, the experimental data are selected to carry out artificial target detection in terrestrial environment and marine ship target detection experiment, and the algorithm is evaluated from three aspects: target detection accuracy, key target detection situation and processing time. Experimental results show that the four detection algorithms designed in this paper have the ability to detect artificial targets in large scene PolSAR images. Among them, the performance of the detection algorithm based on fast Wishart computation is related to the selection of supervisory information, which has a great potential for performance improvement. Three detection algorithms based on the feature of image uniformity description factor can complete the task of artificial target detection in a relatively short time and the detection accuracy is higher than that of the traditional method. The three detection algorithms have different usage of image information, and their detection performance and processing speed are quite different, which can meet different requirements of accuracy and efficiency.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN957.52

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