發(fā)布時(shí)間：2018-05-23 22:57

  本文選題：Landsat衛(wèi)星 + 多光譜圖像　； 參考：《安徽大學(xué)》2014年碩士論文

【摘要】：Landsat衛(wèi)星影像廣泛應(yīng)用在資源調(diào)查、農(nóng)業(yè)生產(chǎn)、環(huán)境監(jiān)測(cè)、生態(tài)保護(hù)等領(lǐng)域。由于受到天氣條件的影響,影像往往存在一些區(qū)域被云層覆蓋,嚴(yán)重影響了影像的判讀。準(zhǔn)確地檢測(cè)出Landsat衛(wèi)星影像中的云層,對(duì)影像后續(xù)的分類、識(shí)別以及目標(biāo)檢測(cè)等進(jìn)一步處理和應(yīng)用具有重要的意義。 本文通過提取Landsat衛(wèi)星多光譜圖像云層和地物特征,針對(duì)不同的云層檢測(cè)應(yīng)用需求,利用不同的支持向量機(jī)分類算法,對(duì)Landsat衛(wèi)星圖像的云檢測(cè)問題進(jìn)行了研究。主要的研究?jī)?nèi)容及研究成果包括以下幾個(gè)方面： 1.介紹了Landsat衛(wèi)星遙感圖像云檢測(cè)的研究目的與意義、國(guó)內(nèi)外研究現(xiàn)狀等。簡(jiǎn)介了支持向量機(jī)分類原理和孿生支持向量機(jī)算法。 2.針對(duì)單波段或者部分波段Landsat衛(wèi)星多光譜圖像,提出一種基于最小二乘孿生支持向量機(jī)的云檢測(cè)方法。先根據(jù)云在不同波段中的大氣輻射特點(diǎn),結(jié)合Landsat7ETM+影像數(shù)據(jù)的光譜特性獲得像元的光譜特征；再通過提取每個(gè)圖像塊的灰度共生矩陣得到相應(yīng)像元點(diǎn)的紋理結(jié)構(gòu)特征,以像元的光譜特性和紋理結(jié)構(gòu)特征構(gòu)造特征向量；最后利用最小二乘孿生支持向量機(jī)分類器進(jìn)行Landsat7ETM+影像像元的云層檢測(cè),實(shí)驗(yàn)結(jié)果表明了方法的有效性。 3.針對(duì)多波段Landsat衛(wèi)星多光譜圖像,提出一種基于ACCA和WSVM相結(jié)合的云檢測(cè)方法。首先利用ACCA方法對(duì)Landsat遙感圖像進(jìn)行云檢測(cè),將圖像像元分成云像元、非云像元和待定像元；然后從Landsat遙感圖像數(shù)據(jù)庫(kù)中提取己知內(nèi)容信息的圖像像元點(diǎn)光譜特征作為WSVM的輸入特征向量,通過建立關(guān)于訓(xùn)練樣本中心距離的權(quán)重函數(shù)來獲得樣本的權(quán)重系數(shù)；最后利用改進(jìn)的WSVM方法進(jìn)行影像像元的云層檢測(cè)。實(shí)驗(yàn)結(jié)果表明,該方法將待定像元中ACCA方法難以檢測(cè)的半透明云檢測(cè)出來。 4.研究了MLTK方法和半監(jiān)督學(xué)習(xí)理論,并將其結(jié)合應(yīng)用到遙感圖像云檢測(cè)中。在利用無標(biāo)記簽樣本情況下,提出一種基于MLTK和STSVM的Landsat衛(wèi)星遙感圖像云檢測(cè)方法,該方法先利用MLTK方法對(duì)Landsat圖像進(jìn)行云檢測(cè),將圖像像元分成云像元和其他待定像元,再?gòu)腖andsat遙感圖像數(shù)據(jù)庫(kù)中提取已知內(nèi)容信息、未知內(nèi)容信息的圖像像元點(diǎn)光譜特征和紋理特征作為輸入特征向量訓(xùn)練STSVM,構(gòu)造最優(yōu)分類超平面,最終將MLTK方法難以檢測(cè)的薄云檢測(cè)出來。仿真實(shí)驗(yàn)結(jié)果表明,與TSVM等方法相比較,該方法所獲得的云檢測(cè)結(jié)果在視覺效果上和定量評(píng)價(jià)上有明顯提高。
[Abstract]:Landsat satellite images are widely used in resource survey, agricultural production, environmental monitoring, ecological protection and so on. Due to the influence of weather conditions, some areas are often covered by clouds, which seriously affect the interpretation of images. The accurate detection of clouds in Landsat satellite images is of great significance for the further processing and application of image classification, recognition and target detection. In this paper, the cloud and ground features of Landsat satellite multispectral images are extracted, and the cloud detection problem of Landsat satellite images is studied by using different support vector machine (SVM) classification algorithms for different cloud detection applications. The main research contents and results include the following aspects: 1. The purpose and significance of cloud detection in Landsat satellite remote sensing image are introduced. This paper introduces the classification principle of support vector machine and the algorithm of twin support vector machine. 2. A cloud detection method based on least square twin support vector machine (LS-TSVM) is proposed for multi-spectral images of single-band or part-band Landsat satellites. Firstly, according to the atmospheric radiation characteristics of cloud in different bands, combining with the spectral characteristics of Landsat7ETM image data, the spectral characteristics of pixels are obtained, and then the texture features of corresponding pixel points are obtained by extracting the gray level co-occurrence matrix of each image block. The spectral and texture characteristics of the pixel are used to construct the feature vector. Finally, the cloud detection of the pixel in Landsat7ETM image is performed by using the least square twin support vector machine classifier. The experimental results show the effectiveness of the method. 3. A cloud detection method based on ACCA and WSVM is proposed for multispectral images of multi-band Landsat satellite. Firstly, the image pixel is divided into cloud pixel, non-cloud pixel and undetermined pixel by using ACCA method for cloud detection of Landsat remote sensing image. Then the image pixel spectral feature of the known content information is extracted from the Landsat remote sensing image database as the input feature vector of WSVM, and the weight coefficient of the sample is obtained by establishing the weight function about the distance of the training sample center. Finally, the improved WSVM method is used for cloud detection of image pixels. The experimental results show that this method can detect the translucent cloud which is difficult to detect by ACCA method in undetermined pixels. 4. The MLTK method and semi-supervised learning theory are studied and applied to cloud detection of remote sensing images. In this paper, a cloud detection method for Landsat satellite remote sensing image based on MLTK and STSVM is proposed in the case of unlabeled sample. Firstly, cloud detection of Landsat image is carried out by using MLTK method, and the image pixel is divided into cloud pixel and other undetermined pixel. Then the known content information is extracted from the Landsat remote sensing image database. The pixel spectral feature and texture feature of unknown content information are trained as input feature vectors to construct the optimal classification hyperplane. Finally, the thin cloud which is difficult to detect by MLTK method is detected. The simulation results show that compared with the TSVM method, the cloud detection results obtained by this method are obviously improved in visual effect and quantitative evaluation.
【學(xué)位授予單位】：安徽大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP751

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