本文選題：過(guò)采樣 + 目標(biāo)檢測(cè)　； 參考：《國(guó)防科學(xué)技術(shù)大學(xué)》2015年碩士論文

【摘要】：紅外弱小目標(biāo)檢測(cè)是目標(biāo)檢測(cè)領(lǐng)域的一項(xiàng)重難點(diǎn)問(wèn)題。由于弱小目標(biāo)信噪比比較低,淹沒(méi)在背景和噪聲中,檢測(cè)難度大。本文研究了過(guò)采樣體制下的目標(biāo)檢測(cè)算法。首先建立過(guò)采樣掃描紅外圖像模型,并分析了紅外弱小目標(biāo)特性。其次介紹了幾種典型的背景抑制算法,并對(duì)空域高通濾波,均值濾波,最大中值濾波進(jìn)行詳細(xì)的介紹。再次,用矩形結(jié)構(gòu)元素及線性多結(jié)構(gòu)元素形態(tài)學(xué)抑制背景,通過(guò)分析:線性多結(jié)構(gòu)元素可以抑制云層背景邊緣,但無(wú)法消除小于目標(biāo)的亮點(diǎn)噪聲,而矩形結(jié)構(gòu)元素對(duì)云層背景邊緣抑制效果較差,但是可以消除小于目標(biāo)的亮點(diǎn)噪聲。在此基礎(chǔ)上,利用過(guò)采樣圖像中目標(biāo)的特點(diǎn),結(jié)合線性多結(jié)構(gòu)元素和矩形結(jié)構(gòu)元素的優(yōu)點(diǎn),提出了基于過(guò)采樣體制的形態(tài)學(xué)多級(jí)濾波背景抑制算法,并對(duì)結(jié)構(gòu)元素進(jìn)行最優(yōu)化構(gòu)造。同時(shí)對(duì)該算法的結(jié)構(gòu)及原理進(jìn)行了具體地闡述,并通過(guò)仿真對(duì)算法進(jìn)行分析。仿真實(shí)驗(yàn)得出該算法在提高過(guò)采樣掃描圖像的信噪比方面效果是比較好的,而且候選目標(biāo)點(diǎn)也比較少,算法的檢測(cè)能力明顯較高,在多幀檢測(cè)中,對(duì)傳統(tǒng)的鄰域判決法進(jìn)行改進(jìn),提出基于聯(lián)合狀態(tài)估計(jì)的過(guò)采樣目標(biāo)序列檢測(cè)算法,采用改進(jìn)的算法對(duì)單幀檢測(cè)階段檢測(cè)出的目標(biāo)進(jìn)行軌跡檢測(cè),改進(jìn)的算法不僅檢測(cè)出了目標(biāo)軌跡,而且虛警率也較低,同時(shí)驗(yàn)證了單幀檢測(cè)中提出的基于過(guò)采樣體制的形態(tài)學(xué)多級(jí)濾波背景抑制算法是可行的。
[Abstract]:Infrared small and weak target detection is an important and difficult problem in the field of target detection. Because the signal-to-noise ratio of small target is low, it is difficult to detect because it is submerged in background and noise. In this paper, the target detection algorithm under oversampling system is studied. Firstly, the oversampling scanning infrared image model is established, and the characteristics of infrared dim targets are analyzed. Secondly, several typical background suppression algorithms are introduced, and the spatial high pass filter, mean value filter and maximum median filter are introduced in detail. Thirdly, the morphology of rectangular structure element and linear multi-structure element is used to suppress the background. Through analysis, the linear multi-structure element can suppress the edge of cloud background, but it can not eliminate the bright spot noise which is smaller than the target. The rectangular structure element has a poor suppression effect on the cloud background edge, but it can eliminate the bright spot noise which is smaller than the target. On this basis, taking advantage of the characteristics of targets in over-sampled images and combining the advantages of linear multi-structure elements and rectangular structural elements, a multi-level morphological filtering background suppression algorithm based on over-sampling scheme is proposed. The structure elements are constructed optimally. At the same time, the structure and principle of the algorithm are described in detail, and the algorithm is analyzed by simulation. The simulation results show that the algorithm is effective in improving the signal-to-noise ratio of the over-sampled scanned images, and the candidate target points are less, the detection ability of the algorithm is obviously higher, in the multi-frame detection, The traditional neighborhood decision method is improved, and an oversampling target sequence detection algorithm based on joint state estimation is proposed. The improved algorithm is used to detect the track of the target detected in single frame detection stage. The improved algorithm not only detects the trajectory of the target, but also has a low false alarm rate. At the same time, it verifies the feasibility of the multi-level morphological filtering background suppression algorithm based on over-sampling in single-frame detection.
【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】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：TP391.41;TN219

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本文編號(hào)：2036628