多時(shí)相遙感圖像變化檢測(cè)技術(shù)研究
發(fā)布時(shí)間：2018-10-23 16:35
【摘要】：變化檢測(cè)作為遙感圖像分析中的一項(xiàng)重要應(yīng)用,為環(huán)境監(jiān)測(cè)、資源勘探、災(zāi)害救援與治理提供了有效的技術(shù)手段。近二十年來(lái),遙感圖像的變化檢測(cè)方法雖然不斷更新,但變化檢測(cè)仍然受到不同因素的影響。大量的研究試圖尋找各種新的遙感圖像變化檢測(cè)方法,但目前為止,并沒(méi)有一種通用方法能夠?qū)Σ煌瑮l件、不同情況的應(yīng)用給出完全滿(mǎn)意的結(jié)果。針對(duì)目前現(xiàn)有的兩時(shí)相變化檢測(cè)方法存在的問(wèn)題和局限性,本文進(jìn)行了以下研究： 首先,針對(duì)標(biāo)準(zhǔn)馬爾科夫隨機(jī)場(chǎng)(MRF)方法存在的先驗(yàn)?zāi)芰亢退迫荒芰恐g采用同樣不變權(quán)重的問(wèn)題,提出了一種基于自適應(yīng)權(quán)重MRF模型的變化檢測(cè)方法。該方法首先對(duì)于圖像進(jìn)行細(xì)節(jié)特征的提取,將圖像中的細(xì)節(jié)特征位置進(jìn)行判別。將不屬于圖像細(xì)節(jié)特征的位置賦予較大權(quán)重于先驗(yàn)?zāi)芰?而將屬于圖像細(xì)節(jié)特征的位置賦以較小權(quán)重于先驗(yàn)?zāi)芰�。該方法首先基�?鄰域線(xiàn)過(guò)程提取邊緣像素點(diǎn)；然后規(guī)定了自適應(yīng)權(quán)重函數(shù)(AWF)的條件,并列舉了8個(gè)AWF的例子；最后對(duì)于多時(shí)相遙感圖像進(jìn)行了實(shí)驗(yàn)以驗(yàn)證本方法的有效性。 針對(duì)標(biāo)準(zhǔn)EM參數(shù)估計(jì)方法不考慮像素間的鄰域信息,易受噪聲影響,參數(shù)估計(jì)不精確的問(wèn)題,提出了一種基于證據(jù)理論的EM參數(shù)估計(jì)方法并將其應(yīng)用于變化檢測(cè)。為了能夠在參數(shù)估計(jì)的過(guò)程中利用鄰域信息,本文將Dempster-Shafer證據(jù)理論(DST)集成于文獻(xiàn)[14]中的EM算法中,使每一步參數(shù)的迭代更新不僅取決于當(dāng)前中心像素的亮度,還取決于其鄰域像素的亮度。從而得到一種基于DST的EM方法(EEM)。為進(jìn)一步提高變化檢測(cè)精度,本文采用最大后驗(yàn)估計(jì)(MAP)標(biāo)記方法對(duì)于EEM算法的結(jié)果進(jìn)行MAP標(biāo)記。假設(shè)差值圖像的類(lèi)別標(biāo)記滿(mǎn)足局部光滑條件,根據(jù)EEM算法得到的參數(shù)和初始標(biāo)記,經(jīng)過(guò)迭代MAP標(biāo)記更新得到最終結(jié)果。實(shí)驗(yàn)結(jié)果表明,MAP標(biāo)記方法的噪聲抑制能力強(qiáng)于EEM。 針對(duì)標(biāo)準(zhǔn)ACM模型不適用于合成孔徑雷達(dá)(SAR)圖像變化檢測(cè)的問(wèn)題,提出了一種基于廣義高斯分布和活動(dòng)輪廓模型的SAR圖像變化檢測(cè)方法。由于SAR圖像通常受乘性斑點(diǎn)噪聲的影響,傳統(tǒng)的C-V活動(dòng)輪廓模型假設(shè)圖像為分段光滑,這與SAR圖像數(shù)據(jù)性質(zhì)相違背,因此不能直接應(yīng)用于SAR圖像變化檢測(cè)。本文將C-V活動(dòng)輪廓模型推廣到廣義高斯混合模型假設(shè)下,得到一種基于廣義高斯分布和ACM模型的SAR圖像變化檢測(cè)方法,并驗(yàn)證了其有效性。 針對(duì)通常采用的CVA方法丟失光譜特征空間信息的問(wèn)題,提出了一種基于平穩(wěn)小波和集成活動(dòng)輪廓模型的多譜多時(shí)相遙感圖像變化檢測(cè)方法。該方法將光譜變化矢量特征空間看做2維黎曼流形嵌入到2+B維高維流形,其中B是光譜波段數(shù)。變化矢量圖像的分割通過(guò)流形上的曲線(xiàn)演化,即IAC完成。IAC模型結(jié)合了測(cè)地活動(dòng)輪廓模型(GAC)和無(wú)邊緣活動(dòng)輪廓模型(C-V模型)兩者的優(yōu)點(diǎn),提高了檢測(cè)精度。 以上方法均使用模擬數(shù)據(jù)集或真實(shí)多時(shí)相遙感圖像數(shù)據(jù)集進(jìn)行了實(shí)驗(yàn)驗(yàn)證。實(shí)驗(yàn)結(jié)果表明本文提出方法的檢測(cè)精度與其他主流方法相當(dāng),大部分結(jié)果優(yōu)于其他主流方法。
[Abstract]:Change detection is an important application in remote sensing image analysis, which provides effective technical means for environmental monitoring, resource exploration, disaster rescue and treatment. In recent twenty years, the change detection methods of remote sensing images have been continuously updated, but change detection is still affected by different factors. A lot of research attempts to find a variety of new methods for change detection of remote sensing images, but so far, none of the common methods can give full satisfaction to different conditions and applications. In view of the existing problems and limitations of the existing two-time phase change detection method, the following research has been carried out: First, the same constant weight is used between the apriori energy and the quasi-random energy present in the standard Markov random field (MRF) method. In this paper, a change detection method based on adaptive weight MRF model is proposed The method comprises the following steps: firstly, extracting the detail feature of the image, and carrying out the detail feature position in the image; a position assigned to the image detail feature is assigned a smaller weight to a priori, The method comprises the following steps: firstly, extracting edge pixel points based on the 8-neighborhood line process; then defining the conditions of the adaptive weight function (AWF) and enumerating eight AWF examples; and finally, carrying out experiments on the multi-time-phase remote sensing image to verify the method In this paper, we propose an EM parameter estimation method based on evidence theory and its application to the problem of neighborhood information between pixels, which is susceptible to noise and inaccurate parameter estimation. In order to be able to utilize neighborhood information in the process of parameter estimation, Dempster-Shafer Evidence Theory (DST) is integrated in the EM algorithm in the literature[14], so that the iterative updating of each step depends not only on the brightness of the current center pixel, but also on its neighborhood. The brightness of the pixels, and thus a DST-based EM method is obtained. (EEM). In order to further improve the accuracy of change detection, the maximum post-test (MAP) marking method is used in this paper. MAP flag. If the category flag of the difference image satisfies the local smooth condition, the parameter and the initial mark obtained according to the EEM algorithm are updated by the iterative MAP mark. Finally, the experimental results show that the noise suppression ability of MAP marking method It is stronger than EEM. Aiming at the problem that the standard ACM model is not applicable to the image change detection of Synthetic Aperture Radar (SAR), a SAR based on generalized Gaussian distribution and active contour model is proposed. Because the SAR image is usually influenced by multiplicative speckle noise, the traditional C-V active contour model assumes that the image is piecewise smooth, which is contrary to the SAR image data property, and therefore cannot be directly applied to S. In this paper, we generalize the C-V active contour model to the generalized Gaussian mixture model, and obtain a SAR image change detection method based on the generalized Gaussian distribution and ACM model. In order to solve the problem of loss of spectral characteristic spatial information by CVA method, a multi-spectral multi-spectral model based on stationary small wave and integrated active contour model is proposed. In this method, the spectral change vector feature space is regarded as a 2 + B-dimensional high-dimensional stream. and B is the number of spectral bands. The curve evolution, i.e. the completion of the curve, incorporates both the geodynamic contour model (GAC) and the borderless active contour model (C-V model). The method has the advantages of improving the detection precision, The experimental results show that the detection precision of the method is equivalent to that of other methods.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TP751
本文編號(hào)：2289836
[Abstract]:Change detection is an important application in remote sensing image analysis, which provides effective technical means for environmental monitoring, resource exploration, disaster rescue and treatment. In recent twenty years, the change detection methods of remote sensing images have been continuously updated, but change detection is still affected by different factors. A lot of research attempts to find a variety of new methods for change detection of remote sensing images, but so far, none of the common methods can give full satisfaction to different conditions and applications. In view of the existing problems and limitations of the existing two-time phase change detection method, the following research has been carried out: First, the same constant weight is used between the apriori energy and the quasi-random energy present in the standard Markov random field (MRF) method. In this paper, a change detection method based on adaptive weight MRF model is proposed The method comprises the following steps: firstly, extracting the detail feature of the image, and carrying out the detail feature position in the image; a position assigned to the image detail feature is assigned a smaller weight to a priori, The method comprises the following steps: firstly, extracting edge pixel points based on the 8-neighborhood line process; then defining the conditions of the adaptive weight function (AWF) and enumerating eight AWF examples; and finally, carrying out experiments on the multi-time-phase remote sensing image to verify the method In this paper, we propose an EM parameter estimation method based on evidence theory and its application to the problem of neighborhood information between pixels, which is susceptible to noise and inaccurate parameter estimation. In order to be able to utilize neighborhood information in the process of parameter estimation, Dempster-Shafer Evidence Theory (DST) is integrated in the EM algorithm in the literature[14], so that the iterative updating of each step depends not only on the brightness of the current center pixel, but also on its neighborhood. The brightness of the pixels, and thus a DST-based EM method is obtained. (EEM). In order to further improve the accuracy of change detection, the maximum post-test (MAP) marking method is used in this paper. MAP flag. If the category flag of the difference image satisfies the local smooth condition, the parameter and the initial mark obtained according to the EEM algorithm are updated by the iterative MAP mark. Finally, the experimental results show that the noise suppression ability of MAP marking method It is stronger than EEM. Aiming at the problem that the standard ACM model is not applicable to the image change detection of Synthetic Aperture Radar (SAR), a SAR based on generalized Gaussian distribution and active contour model is proposed. Because the SAR image is usually influenced by multiplicative speckle noise, the traditional C-V active contour model assumes that the image is piecewise smooth, which is contrary to the SAR image data property, and therefore cannot be directly applied to S. In this paper, we generalize the C-V active contour model to the generalized Gaussian mixture model, and obtain a SAR image change detection method based on the generalized Gaussian distribution and ACM model. In order to solve the problem of loss of spectral characteristic spatial information by CVA method, a multi-spectral multi-spectral model based on stationary small wave and integrated active contour model is proposed. In this method, the spectral change vector feature space is regarded as a 2 + B-dimensional high-dimensional stream. and B is the number of spectral bands. The curve evolution, i.e. the completion of the curve, incorporates both the geodynamic contour model (GAC) and the borderless active contour model (C-V model). The method has the advantages of improving the detection precision, The experimental results show that the detection precision of the method is equivalent to that of other methods.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TP751
【參考文獻(xiàn)】
相關(guān)期刊論文 前9條
1 鐘家強(qiáng);王潤(rùn)生;;基于線(xiàn)特征的多時(shí)相遙感圖像變化檢測(cè)[J];國(guó)防科技大學(xué)學(xué)報(bào);2006年05期
2 宋翠玉;李培軍;楊鋒杰;;運(yùn)用多尺度圖像紋理進(jìn)行城市擴(kuò)展變化檢測(cè)[J];國(guó)土資源遙感;2006年03期
3 王東廣;肖鵬峰;宋曉群;王鐵成;陳剛;;結(jié)合紋理信息的高分辨率遙感圖像變化檢測(cè)方法[J];國(guó)土資源遙感;2012年04期
4 程詠梅,潘泉,張洪才,王剛;信息融合圖像識(shí)別算法及其在三維飛機(jī)圖像識(shí)別中的應(yīng)用研究[J];航空學(xué)報(bào);2004年02期
5 辛芳芳;焦李成;王桂婷;萬(wàn)紅林;;基于小波域Fisher分類(lèi)器的SAR圖像變化檢測(cè)[J];紅外與毫米波學(xué)報(bào);2011年02期
6 張軍;肖剛;敬忠良;;基于邊緣增強(qiáng)的遙感圖像變化檢測(cè)技術(shù)[J];計(jì)算機(jī)工程與應(yīng)用;2008年26期
7 張永梅;李立鵬;姜明;劉海偉;;綜合像素級(jí)和特征級(jí)的建筑物變化檢測(cè)方法[J];計(jì)算機(jī)科學(xué);2013年01期
8 李青松;覃錫忠;賈振紅;楊杰;胡英杰;;基于NSCT和FCM聚類(lèi)的SAR圖像無(wú)監(jiān)督變化檢測(cè)[J];激光雜志;2013年04期
9 秦永;孔維華;曹俊茹;宋偉東;;基于SVM的遙感影像土地利用變化檢測(cè)方法[J];濟(jì)南大學(xué)學(xué)報(bào)(自然科學(xué)版);2010年01期
，本文編號(hào)：2289836
本文鏈接：http://sikaile.net/guanlilunwen/gongchengguanli/2289836.html
最近更新
教材專(zhuān)著
