多時(shí)相遙感圖像融合去噪方法研究
發(fā)布時(shí)間：2018-08-07 19:34
【摘要】：遙感技術(shù)的出現(xiàn),使我們能不與研究對(duì)象直接接觸,通過(guò)傳感設(shè)備來(lái)獲取觀察對(duì)象的基本信息。這就避免了一些偏遠(yuǎn)或險(xiǎn)峻的地區(qū)信息無(wú)法取得的情況,成為至今為止全球范圍內(nèi)動(dòng)態(tài)觀測(cè)數(shù)據(jù)的唯一方式,被廣泛應(yīng)用到多個(gè)領(lǐng)域,對(duì)經(jīng)濟(jì)的增長(zhǎng)和社會(huì)的發(fā)展起著很大的催化作用。然而,由于受天氣、遙感設(shè)備及傳輸介質(zhì)的影響,遙感圖像在成像和傳輸?shù)倪^(guò)程中,往往會(huì)受到很多噪聲的影響,其中最為常見(jiàn)的噪聲為高斯噪聲、云噪聲和霧噪聲等。這些噪聲的存在,將直接影響遙感圖像的進(jìn)一步處理、分析及應(yīng)用,影響數(shù)據(jù)的使用價(jià)值。遙感圖像去噪的目標(biāo)在于在保護(hù)圖像細(xì)節(jié)信息的前提下,最大限度地去除噪聲,提高數(shù)據(jù)的可讀性與有效性。目前,對(duì)于熱噪聲、散粒噪聲等高斯噪聲的處理,主要是針對(duì)單幅遙感圖像,利用噪聲在空間域或頻域的特征,對(duì)遙感圖像進(jìn)行降噪處理。但這類去噪方法存在一個(gè)問(wèn)題,即保留圖像邊緣與去除噪聲的矛盾,往往會(huì)出現(xiàn)圖像邊緣信息被過(guò)度扼殺,造成邊緣模糊或去除噪聲不理想現(xiàn)象。針對(duì)云噪聲,對(duì)于薄云,由于它不僅包含了與云相關(guān)的信息,還包含了地物等有效信息,對(duì)它的研究也比較多,常用的處理方式是削弱云信息,同時(shí)增強(qiáng)地物信息,使地物清晰。而對(duì)于厚云,由于地物信息被完全遮蓋,幾乎不含有用信息,使用單幅遙感圖像去除厚云往往會(huì)引起信息空洞。這說(shuō)明單幅遙感圖像的信息量不足,需要將不同時(shí)間同一地區(qū)具有互補(bǔ)信息的多時(shí)相遙感數(shù)據(jù)根據(jù)一定的方法,有效的結(jié)合起來(lái),得到一幅信息量更多的遙感圖像。針對(duì)以上分析,本文研究了基于DS(Dempster-Shafer)證據(jù)理論的多時(shí)相遙感圖像融合去噪方法,主要從以下3個(gè)方面展開(kāi):(1)分析了遙感圖像中多類噪聲的特點(diǎn)與研究現(xiàn)狀,并分析了DS證據(jù)理論在多時(shí)相遙感圖像融合去噪的可行性:DS證據(jù)理論作為一種推理理論,屬于人工智能的范疇,它能融合多個(gè)證據(jù)并做出決策,對(duì)推理給出合理的闡釋,可以有效解決由于對(duì)研究對(duì)象認(rèn)知的不準(zhǔn)確或認(rèn)知缺失所造成的不確定性問(wèn)題。遙感圖像中,噪聲具有隨機(jī)性與不確定性,而DS證據(jù)理論能綜合考慮來(lái)自多源的不確定信息,同樣適合用在多時(shí)相遙感圖像融合去噪過(guò)程中。(2)提出了基于DS證據(jù)理論的多時(shí)相遙感圖像融合去除高斯噪聲的方法,根據(jù)DS證據(jù)理論的基本原理,為獲取證據(jù)的基本概率分配,設(shè)計(jì)四個(gè)高斯噪聲檢測(cè)模型,即兩狀態(tài)高斯混合模型、均值檢測(cè)模型、中值檢測(cè)模型、邊緣分析模型,用于分析每個(gè)灰度值與噪聲相關(guān)還是與地物相關(guān)。然后根據(jù)DS證據(jù)理論融合規(guī)則,將各幅遙感圖像四個(gè)證據(jù)融合成一個(gè)整體,得到每幅遙感圖像各像素與噪聲相關(guān)或與地物相關(guān)總的證據(jù)。接著利用DS證據(jù)理論將多時(shí)相遙感圖像的多個(gè)證據(jù)合成,得到最終結(jié)論。最后根據(jù)所得的結(jié)論與決策規(guī)則,對(duì)遙感圖像進(jìn)行去噪處理。實(shí)驗(yàn)結(jié)果表明,該算法在高斯噪聲去除、圖像邊緣保持等方面優(yōu)于傳統(tǒng)的單幅遙感圖像去噪算法,圖像方差、信噪比和視覺(jué)效果方面都有所改進(jìn)。(3)提出了基于DS證據(jù)理論的多時(shí)相遙感圖像融合去除云噪聲的方法,根據(jù)DS證據(jù)理論的基本原理,為獲取證據(jù)的基本概率分配,設(shè)計(jì)兩個(gè)云噪聲檢測(cè)模型,分別依據(jù)灰度統(tǒng)計(jì)值變化和頻域信息變化。首先將多時(shí)相遙感圖像按同樣的標(biāo)準(zhǔn)分割成若干小區(qū)域,每個(gè)小區(qū)域按照以上兩個(gè)模型,判斷每個(gè)區(qū)域與云相關(guān)還是與地物相關(guān)。然后根據(jù)DS證據(jù)理論合成規(guī)則,將各幅遙感圖像兩個(gè)證據(jù)融合成一個(gè)整體,得到每幅遙感圖像各小區(qū)域與云相關(guān)或與地物相關(guān)總的證據(jù)。接著利用DS證據(jù)理論將多時(shí)相遙感圖像的多個(gè)證據(jù)合并,得到最終結(jié)論。最后根據(jù)所得的結(jié)論與決策規(guī)則,對(duì)遙感圖像進(jìn)行融合去云。實(shí)驗(yàn)結(jié)果表明,該算法在云噪聲去除方面,通過(guò)利用有效互補(bǔ)信息,得到了信息更加豐富的圖像。
[Abstract]:The emergence of remote sensing technology makes it possible for us to get the basic information of the observation objects without direct contact with the research objects. This avoids the information that the remote or steep regional information can't obtain. It has become the only way to date the dynamic observation data in the world so far, and has been widely used in many fields. However, because of the influence of weather, remote sensing equipment and transmission medium, remote sensing images are often affected by a lot of noise in the process of imaging and transmission. The most common noise is Gauss noise, cloud noise and fog noise. The existence of these noises will be direct. The further processing, analysis and application of remote sensing images affect the use value of the data. The target of remote sensing image denoising is to remove the noise and improve the readability and effectiveness of the data on the premise of protecting the details of the image. At present, the processing of Gauss noise, such as thermal noise and granular noise, is mainly aimed at the single. The remote sensing image is used to denoise the remote sensing image by using the characteristics of noise in space or frequency domain. However, there is a problem in this kind of denoising method, that is, to retain the edge of the image and to remove the noise, the edge information of the image is often excessively stifled, causing edge paste or removing noise is not ideal. Because it contains not only the information related to the cloud, but also the effective information such as ground objects, it also has more research on it. The common processing method is to weaken the cloud information, and to enhance the information of the ground, and make the ground objects clear. For thick clouds, because the information of the ground is completely covered, almost no useful information is contained, and a single remote sensing image is used. In addition to the thick cloud, it often causes information void. This shows that the information of single remote sensing images is insufficient. It is necessary to combine the multi temporal remote sensing data with complementary information at different time and the same area according to a certain method to get a more remote sensing image. Afer) the multi temporal remote sensing image fusion denoising method of evidence theory is mainly carried out from the following 3 aspects: (1) analyzing the characteristics and research status of multi class noise in remote sensing images, and analyzing the feasibility of DS evidence theory in multi temporal remote sensing image fusion denoising: DS evidence theory is a kind of reasoning theory, which belongs to the category of artificial intelligence. It can integrate a number of evidence and make decisions and give a reasonable explanation to the reasoning, which can effectively solve the uncertainty caused by the inaccuracy or lack of cognition of the research objects. In remote sensing images, the noise is random and uncertain, and the DS evidence theory can consider the uncertain information from multiple sources. In the process of multi temporal remote sensing image fusion de-noising. (2) a method of multi temporal remote sensing image fusion based on DS evidence theory is proposed to remove Gauss noise. According to the basic principle of DS evidence theory, the basic probability distribution of evidence is obtained, and four Gauss noise detection models are designed, that is, the two state Gauss mixture model and the mean detection model. Type, median detection model, edge analysis model, which are used to analyze the correlation of each gray value to noise or related to ground objects. Then, according to the fusion rules of DS evidence theory, four evidence of each remote sensing image is fused into a whole, and the total evidence of each pixel and noise related to or related to the ground objects is obtained in each remote sensing image. Then the DS evidence is used. In the theory, the multi temporal remote sensing images are synthesized and the final conclusion is obtained. Finally, the remote sensing image is de-noised according to the conclusions and the decision rules. The experimental results show that the algorithm is superior to the traditional single amplitude remote sensing image denoising algorithm, image variance, signal to noise ratio and view in Gauss noise removal and image edge preservation. The sense effect has been improved. (3) a method of multi temporal remote sensing image fusion based on DS evidence theory is proposed to remove cloud noise. According to the basic principle of DS evidence theory, the basic probability distribution of evidence is obtained, and two cloud noise detection models are designed, according to the change of gray level statistics and the change of frequency domain information respectively. Remote sensing images are divided into small areas according to the same standard. Each area is based on the above two models to determine whether each region is related to the cloud or the ground objects. Then, according to the DS evidence theory, the rules are synthesized and the two evidence of each remote sensing image is fused into a whole, and each area of the remote sensing image is related to or with the cloud. General evidence related to things. Then, using the DS evidence theory, multiple evidence of multi phase remote sensing image is merged and the final conclusion is obtained. Finally, the remote sensing image is fused to cloud based on the conclusions and the decision rules. The experimental results show that the algorithm can get more information through the use of effective complementary information in the removal of cloud noise, and the information is more abundant. A rich image.
【學(xué)位授予單位】：上海海洋大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP751
[Abstract]:The emergence of remote sensing technology makes it possible for us to get the basic information of the observation objects without direct contact with the research objects. This avoids the information that the remote or steep regional information can't obtain. It has become the only way to date the dynamic observation data in the world so far, and has been widely used in many fields. However, because of the influence of weather, remote sensing equipment and transmission medium, remote sensing images are often affected by a lot of noise in the process of imaging and transmission. The most common noise is Gauss noise, cloud noise and fog noise. The existence of these noises will be direct. The further processing, analysis and application of remote sensing images affect the use value of the data. The target of remote sensing image denoising is to remove the noise and improve the readability and effectiveness of the data on the premise of protecting the details of the image. At present, the processing of Gauss noise, such as thermal noise and granular noise, is mainly aimed at the single. The remote sensing image is used to denoise the remote sensing image by using the characteristics of noise in space or frequency domain. However, there is a problem in this kind of denoising method, that is, to retain the edge of the image and to remove the noise, the edge information of the image is often excessively stifled, causing edge paste or removing noise is not ideal. Because it contains not only the information related to the cloud, but also the effective information such as ground objects, it also has more research on it. The common processing method is to weaken the cloud information, and to enhance the information of the ground, and make the ground objects clear. For thick clouds, because the information of the ground is completely covered, almost no useful information is contained, and a single remote sensing image is used. In addition to the thick cloud, it often causes information void. This shows that the information of single remote sensing images is insufficient. It is necessary to combine the multi temporal remote sensing data with complementary information at different time and the same area according to a certain method to get a more remote sensing image. Afer) the multi temporal remote sensing image fusion denoising method of evidence theory is mainly carried out from the following 3 aspects: (1) analyzing the characteristics and research status of multi class noise in remote sensing images, and analyzing the feasibility of DS evidence theory in multi temporal remote sensing image fusion denoising: DS evidence theory is a kind of reasoning theory, which belongs to the category of artificial intelligence. It can integrate a number of evidence and make decisions and give a reasonable explanation to the reasoning, which can effectively solve the uncertainty caused by the inaccuracy or lack of cognition of the research objects. In remote sensing images, the noise is random and uncertain, and the DS evidence theory can consider the uncertain information from multiple sources. In the process of multi temporal remote sensing image fusion de-noising. (2) a method of multi temporal remote sensing image fusion based on DS evidence theory is proposed to remove Gauss noise. According to the basic principle of DS evidence theory, the basic probability distribution of evidence is obtained, and four Gauss noise detection models are designed, that is, the two state Gauss mixture model and the mean detection model. Type, median detection model, edge analysis model, which are used to analyze the correlation of each gray value to noise or related to ground objects. Then, according to the fusion rules of DS evidence theory, four evidence of each remote sensing image is fused into a whole, and the total evidence of each pixel and noise related to or related to the ground objects is obtained in each remote sensing image. Then the DS evidence is used. In the theory, the multi temporal remote sensing images are synthesized and the final conclusion is obtained. Finally, the remote sensing image is de-noised according to the conclusions and the decision rules. The experimental results show that the algorithm is superior to the traditional single amplitude remote sensing image denoising algorithm, image variance, signal to noise ratio and view in Gauss noise removal and image edge preservation. The sense effect has been improved. (3) a method of multi temporal remote sensing image fusion based on DS evidence theory is proposed to remove cloud noise. According to the basic principle of DS evidence theory, the basic probability distribution of evidence is obtained, and two cloud noise detection models are designed, according to the change of gray level statistics and the change of frequency domain information respectively. Remote sensing images are divided into small areas according to the same standard. Each area is based on the above two models to determine whether each region is related to the cloud or the ground objects. Then, according to the DS evidence theory, the rules are synthesized and the two evidence of each remote sensing image is fused into a whole, and each area of the remote sensing image is related to or with the cloud. General evidence related to things. Then, using the DS evidence theory, multiple evidence of multi phase remote sensing image is merged and the final conclusion is obtained. Finally, the remote sensing image is fused to cloud based on the conclusions and the decision rules. The experimental results show that the algorithm can get more information through the use of effective complementary information in the removal of cloud noise, and the information is more abundant. A rich image.
【學(xué)位授予單位】：上海海洋大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP751
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