多時相遙感圖像融合去噪方法研究
發(fā)布時間：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象直接接觸,通過傳感設(shè)備來獲取觀察對象的基本信息。這就避免了一些偏遠或險峻的地區(qū)信息無法取得的情況,成為至今為止全球范圍內(nèi)動態(tài)觀測數(shù)據(jù)的唯一方式,被廣泛應用到多個領(lǐng)域,對經(jīng)濟的增長和社會的發(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^程中,往往會受到很多噪聲的影響,其中最為常見的噪聲為高斯噪聲、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一步處理、分析及應用,影響數(shù)據(jù)的使用價值。遙感圖像去噪的目標在于在保護圖像細節(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,對于熱噪聲、散粒噪聲等高斯噪聲的處理,主要是針對單幅遙感圖像,利用噪聲在空間域或頻域的特征,對遙感圖像進行降噪處理。但這類去噪方法存在一個問題,即保留圖像邊緣與去除噪聲的矛盾,往往會出現(xiàn)圖像邊緣信息被過度扼殺,造成邊緣模糊或去除噪聲不理想現(xiàn)象。針對云噪聲,對于薄云,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,對它的研究也比較多,常用的處理方式是削弱云信息,同時增強地物信息,使地物清晰。而對于厚云,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引起信息空洞。這說明單幅遙感圖像的信息量不足,需要將不同時間同一地區(qū)具有互補信息的多時相遙感數(shù)據(jù)根據(jù)一定的方法,有效的結(jié)合起來,得到一幅信息量更多的遙感圖像。針對以上分析,本文研究了基于DS(Dempster-Shafer)證據(jù)理論的多時相遙感圖像融合去噪方法,主要從以下3個方面展開:(1)分析了遙感圖像中多類噪聲的特點與研究現(xiàn)狀,并分析了DS證據(jù)理論在多時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,它能融合多個證據(jù)并做出決策,對推理給出合理的闡釋,可以有效解決由于對研究對象認知的不準確或認知缺失所造成的不確定性問題。遙感圖像中,噪聲具有隨機性與不確定性,而DS證據(jù)理論能綜合考慮來自多源的不確定信息,同樣適合用在多時相遙感圖像融合去噪過程中。(2)提出了基于DS證據(jù)理論的多時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)計四個高斯噪聲檢測模型,即兩狀態(tài)高斯混合模型、均值檢測模型、中值檢測模型、邊緣分析模型,用于分析每個灰度值與噪聲相關(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證據(jù)融合成一個整體,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)理論將多時相遙感圖像的多個證據(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,對遙感圖像進行去噪處理。實驗結(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、信噪比和視覺效果方面都有所改進。(3)提出了基于DS證據(jù)理論的多時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)計兩個云噪聲檢測模型,分別依據(jù)灰度統(tǒng)計值變化和頻域信息變化。首先將多時相遙感圖像按同樣的標準分割成若干小區(qū)域,每個小區(qū)域按照以上兩個模型,判斷每個區(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證據(jù)融合成一個整體,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)理論將多時相遙感圖像的多個證據(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,對遙感圖像進行融合去云。實驗結(jié)果表明,該算法在云噪聲去除方面,通過利用有效互補信息,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.
【學位授予單位】：上海海洋大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：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.
【學位授予單位】：上海海洋大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP751
【參考文獻】
相關(guān)期刊論文 前10條
1 夏琴;邢帥;馬東洋;莫德林;李鵬程;葛忠孝;;遙感衛(wèi)星影像K-SVD稀疏表示去噪[J];遙感學報;2016年03期
2 王睿;韋春桃;馬云棟;胡濤;;基于BP神經(jīng)網(wǎng)絡(luò)的Landsat影像去云方法[J];桂林理工大學學報;2015年03期
3 李嘉浪;李華君;徐慶;;基于小波閾值的非局部均值去噪[J];計算機工程與科學;2015年08期
4 張靖宇;馬毅;田震;梁建;;小波濾噪對多光譜遙感水深反演精度的影響分析[J];海洋科學進展;2015年03期
5 周小軍;郭佳;周承仙;譚薇;;基于改進同態(tài)濾波的遙感圖像去云算法[J];無線電工程;2015年03期
6 周雪s，
本文編號：2171128
本文鏈接：http://www.sikaile.net/kejilunwen/zidonghuakongzhilunwen/2171128.html
最近更新
教材專著
