發(fā)布時(shí)間：2018-01-24 00:56

  本文關(guān)鍵詞： 遙感圖像 變化檢測(cè) 融合 特征提取 PCA Treelet變換 核模糊聚類加權(quán)系數(shù)　出處：《西安電子科技大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：遙感圖像變化檢測(cè)是指通過(guò)對(duì)同一地區(qū)不同時(shí)期的兩幅或多幅遙感圖像進(jìn)行比較分析，根據(jù)圖像之間的差異來(lái)獲取地物的變化信息。遙感圖像變化檢測(cè)技術(shù)已成功地應(yīng)用于眾多領(lǐng)域，如環(huán)境監(jiān)測(cè)、土地利用和土地覆蓋的動(dòng)態(tài)監(jiān)測(cè)、森林或植被的變化分析、災(zāi)害評(píng)估、農(nóng)業(yè)調(diào)查、城鎮(zhèn)變化研究及在軍事中的人造目標(biāo)監(jiān)測(cè)和地面武裝部署分析。 本文介紹了遙感圖像變化檢測(cè)的研究背景以及存在的問題，對(duì)已有變化檢測(cè)技術(shù)進(jìn)行了總結(jié)，并以差異圖融合和特征提取為主要研究?jī)?nèi)容，針對(duì)兩時(shí)相遙感圖像的變化檢測(cè)問題進(jìn)行了研究。 （1）提出了一種基于圖像融合和PCA-核模糊聚類的遙感圖像變化檢測(cè)方法。該方法首先用差值法、對(duì)數(shù)比值法和均值比法構(gòu)造三種不同的差異圖，，然后對(duì)差異圖進(jìn)行融合，對(duì)融合后的圖像進(jìn)行PCA（Principal Component Analysis）特征提取，然后用基于核的模糊聚類將特征聚為兩類。該方法采用圖像融合的方法構(gòu)造差異圖，對(duì)不同類型的遙感圖像均可獲得較好的檢測(cè)結(jié)果，解決了單一類型差異圖檢測(cè)精度低、適用范圍窄的問題，具有較好的魯棒性。該方法對(duì)PCA提取的特征采用基于核的模糊聚類方法，將原始數(shù)據(jù)映射到高維特征空間再進(jìn)行聚類，實(shí)現(xiàn)更為準(zhǔn)確的聚類，進(jìn)一步降低了變化檢測(cè)的錯(cuò)誤率。 （2）提出了一種基于Treelet特征融合的遙感圖像變化檢測(cè)方法，首先用差值法、對(duì)數(shù)比值法和均值比法構(gòu)造三種不同的差異圖，然后用Treelet變換對(duì)三幅不同的差異圖進(jìn)行特征融合。該方法由于采用Treelet變換進(jìn)行特征提取，因而操作簡(jiǎn)單，正確率高，抗噪性能好；該方法由于利用了不同差異圖的有效信息和空間鄰域信息進(jìn)行變化檢測(cè)，進(jìn)一步提高了抗噪性能和變化檢測(cè)精度；此外，該方法對(duì)合成孔徑雷達(dá)（Synthetic Aperture Radar，SAR）圖像和光譜圖像都可以得到滿意的變化檢測(cè)結(jié)果，魯棒性好。 （3）提出了一種基于加權(quán)系數(shù)和非下采樣Contourlet變換（NonsubsampledContourlet Transform, NSCT）特征融合的遙感圖像變化檢測(cè)方法，首先用比值法和差值法構(gòu)造兩種不同的差異圖，然后對(duì)差異圖乘以加權(quán)系數(shù)，然后用NSCT變換分解帶有權(quán)值的差異圖以獲得方向特征，對(duì)方向特征和分解前差異圖的原始灰度特征進(jìn)行聚類得到變化檢測(cè)結(jié)果。該方法有效結(jié)合不同差異圖的信息，并且利用方向特征表達(dá)鄰域信息，具有一定的抗噪能力，克服了單一類型差異圖檢測(cè)效果不好的弊端，提高了變化檢測(cè)準(zhǔn)確度。
[Abstract]:Remote sensing image change detection refers to the comparison and analysis of two or more remote sensing images in different periods of the same area. Remote sensing image change detection technology has been successfully used in many fields, such as environmental monitoring, land use and land cover dynamic monitoring. Analysis of changes in forests or vegetation, disaster assessment, agricultural surveys, urban change studies and surveillance of man-made targets in the military and analysis of armed deployment on the ground. This paper introduces the research background and existing problems of remote sensing image change detection, summarizes the existing change detection technology, and takes the difference image fusion and feature extraction as the main research content. The change detection of 2:00 remote sensing image is studied. In this paper, a method of remote sensing image change detection based on image fusion and PCA-kernel fuzzy clustering is proposed. The difference method, logarithmic ratio method and mean ratio method are used to construct three different images. Then the difference map is fused and the fused image is extracted by PCA(Principal Component Analysis. Then the features are clustered into two categories by kernel based fuzzy clustering. This method uses image fusion method to construct difference map, and better detection results can be obtained for different types of remote sensing images. The method solves the problem of low detection precision and narrow range of application of single type difference map, and has good robustness. The kernel based fuzzy clustering method is used to extract the features of PCA. The original data is mapped to the high-dimensional feature space and then clustered to achieve more accurate clustering, which further reduces the error rate of change detection. In this paper, a method of remote sensing image change detection based on Treelet feature fusion is proposed. Firstly, three different difference maps are constructed by using difference method, logarithmic ratio method and mean ratio method. Then the Treelet transform is used to fuse the features of three different images. Because of the feature extraction using Treelet transform, the method is easy to operate, has high accuracy and good anti-noise performance. Because the effective information of different difference map and the spatial neighborhood information are used to detect the change, the anti-noise performance and the accuracy of the change detection are further improved. In addition, the method can obtain satisfactory change detection results for synthetic Aperture radar synthetic Aperture radar (SAR) images and spectral images. Good robustness. (3) A nonsubsampled Contour Transform based on weighted coefficient and non-down-sampled Contourlet transform is proposed. NSCT feature fusion remote sensing image change detection method, first using the ratio method and difference method to construct two different difference map, and then the difference map multiplied by the weighting coefficient. Then NSCT transform is used to decompose the difference graph with weight to obtain the direction feature. The change detection results are obtained by clustering the original gray features of the difference map and the orientation feature. The method combines the information of the different difference map effectively and expresses the neighborhood information by using the direction feature. It has a certain ability to resist noise, overcomes the shortcoming of single type difference map detection, and improves the accuracy of change detection.
【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】：2014
【分類號(hào)】：TP751

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