本文選題：SAR圖像 + 顯著性區(qū)域檢測��； 參考：《西安電子科技大學(xué)》2015年碩士論文

【摘要】：隨著合成孔徑雷達(dá)(Synthetic Aperture Radar,SAR)成像技術(shù)的成熟和偵察范圍的增加,現(xiàn)有的SAR圖像數(shù)據(jù)量遠(yuǎn)超過了目前解譯SAR圖像所承受的能力,僅僅獲取這些高分辨率的SAR圖像并沒有太多意義,重要的是解譯和提取SAR圖像中的重要信息。那么,如何從海量數(shù)據(jù)中分析并提取出這部分有用的信息就顯得尤為重要,而視覺顯著性區(qū)域檢測的出現(xiàn)為解決這一問題提供了有效方案。由于乘性噪聲的影響,使得SAR圖像在顯著性區(qū)域檢測領(lǐng)域鮮有研究。因此,我們通過對(duì)光學(xué)圖像中許多經(jīng)典算法的研究,選擇了LC(Linear-color Contrast)模型對(duì)SAR圖像進(jìn)行顯著性區(qū)域檢測�？紤]到SAR圖像與光學(xué)圖像的不同成像機(jī)制,不可能將光學(xué)圖像中的方法直接應(yīng)用到SAR圖像中,因此,我們需要在LC模型的基礎(chǔ)上進(jìn)行改進(jìn)。下面介紹一下本文提出的兩種基于LC模型的SAR圖像顯著性區(qū)域檢測算法:第一種算法:基于局部相似度的SAR圖像顯著性區(qū)域檢測算法。由于LC模型突出強(qiáng)調(diào)稀有顏色的特點(diǎn),以及部分SAR圖像顏色復(fù)雜度低、層次分明的特點(diǎn),我們用LC模型對(duì)這一類SAR圖像提取初級(jí)顯著圖。由于SAR圖像乘性噪聲的影響,初級(jí)顯著圖中存在大量噪聲混合在顯著區(qū)域。對(duì)此,我們?cè)诔跫?jí)顯著圖的基礎(chǔ)上,對(duì)目標(biāo)區(qū)域所有像素計(jì)算其與周圍像素的相似度之和。相似度越大,證明像素鄰域內(nèi)多為目標(biāo)點(diǎn),則判定該區(qū)域?yàn)轱@著區(qū)域;相似度越小,證明像素鄰域內(nèi)多為背景點(diǎn),該像素存在于背景中,則判定該區(qū)域?yàn)楸尘皡^(qū)域。接下來,將初級(jí)顯著圖與相似圖相乘,使得最終顯著圖中的顯著區(qū)域得以增強(qiáng),背景區(qū)域得以削弱,以此來減弱噪聲的影響。第二種算法:基于超像素的SAR圖像顯著性區(qū)域檢測算法。首先,用LC模型對(duì)SAR圖像提取初級(jí)顯著圖。然后,用SLIC(simple linear iterative clustering)超像素分割算法對(duì)SAR圖像進(jìn)行超像素分割。計(jì)算每個(gè)超像素的平均灰度,并賦值給超像素中的每個(gè)像素點(diǎn),使得比較尖銳的噪聲與周圍像素融合在一起。這樣,經(jīng)過聚類后的SAR圖像,背景中噪聲的影響明顯減弱。此時(shí),基于背景灰度均勻的特點(diǎn),再運(yùn)用LC模型對(duì)聚類后的SAR圖像進(jìn)行顯著區(qū)域檢測。最后,將該顯著圖與初級(jí)顯著圖相乘,最終也能獲取較高質(zhì)量的顯著圖。
[Abstract]:With the maturity of synthetic Aperture Radar (SAR) imaging technology and the increase of reconnaissance range, the existing SAR image data far exceeds the current ability to interpret SAR images, so it is not meaningful to simply obtain these high-resolution SAR images. It is important to interpret and extract important information from SAR images. So how to analyze and extract the useful information from massive data is particularly important and the emergence of visual salient region detection provides an effective solution to this problem. Due to the effect of multiplicative noise, there is little research on SAR images in the field of significant region detection. Therefore, through the study of many classical algorithms in optical images, we select LC(Linear-color Contrast-based model to detect the significant region of SAR images. Considering the different imaging mechanisms of SAR images and optical images, it is impossible to directly apply the methods in optical images to SAR images. Therefore, we need to improve the LC model. Two significant region detection algorithms for SAR images based on LC model are introduced in this paper. The first one is a significant region detection algorithm for SAR images based on local similarity. Because the LC model highlights the characteristics of rare colors, and some SAR images have low color complexity and distinct layers, we use LC model to extract the primary salient images of this kind of SAR images. Due to the effect of multiplicative noise in SAR images, a large number of noises are mixed in the significant region in the primary salient map. On the basis of the primary salience map, we calculate the sum of the similarity between the pixels in the target region and the surrounding pixels. The larger the similarity is, the more the pixel neighborhood is the target point, the more significant the region is, the smaller the similarity degree is, the more the pixel neighborhood is the background point, the more the pixel exists in the background, the more the region is the background region. Then, by multiplying the primary salience map with the similar map, the significant area in the final significant map is enhanced and the background area is weakened, so as to weaken the effect of noise. The second algorithm: SAR image salience region detection algorithm based on super pixel. Firstly, the primary salient map of SAR image is extracted by LC model. Then, the SLIC(simple linear iterative clustering algorithm is used to segment the SAR image. The average gray scale of each super pixel is calculated and assigned to each pixel in the superpixel to make the sharp noise merge with the surrounding pixels. In this way, after clustering SAR images, the influence of noise in the background is obviously weakened. In this case, based on the uniform gray level of background, the LC model is used to detect the significant regions of the clustered SAR images. Finally, by multiplying the salience map with the primary salience map, a higher quality salience map can be obtained.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TN957.52

【相似文獻(xiàn)】

相關(guān)期刊論文 前10條

1 單列;劉政凱;;顯著區(qū)域檢測的建筑物檢索[J];聲學(xué)技術(shù);2007年06期

2 包瑩瑩;沈夢偉;;基于運(yùn)動(dòng)區(qū)域檢測的黑板設(shè)計(jì)[J];現(xiàn)代計(jì)算機(jī)(專業(yè)版);2009年08期

3 景慧昀;韓琦;牛夏牧;;顯著區(qū)域檢測算法綜述[J];智能計(jì)算機(jī)與應(yīng)用;2014年01期

4 萬夢玉;張夢琪;;基于游戲中區(qū)域檢測碰撞的改進(jìn)措施[J];計(jì)算機(jī)光盤軟件與應(yīng)用;2014年08期

5 王艷娟;陳曉紅;;圖像感興趣區(qū)域檢測技術(shù)[J];科技資訊;2006年32期

6 王艷娟;陳曉紅;黃曉欣;;圖像感興趣區(qū)域檢測技術(shù)[J];計(jì)算機(jī)與數(shù)字工程;2007年05期

7 陳碩;于曉升;吳成東;陳東岳;;用于場景分類的顯著建筑物區(qū)域檢測[J];上海交通大學(xué)學(xué)報(bào);2011年08期

8 肖創(chuàng)柏;司薇;鄧米克;王首道;;非感興趣區(qū)域時(shí)空分析的運(yùn)動(dòng)顯著區(qū)域檢測[J];北京工業(yè)大學(xué)學(xué)報(bào);2012年01期

9 蔣鵬;秦小麟;劉s，

本文編號(hào)：1805940