【摘要】：變化檢測技術(shù)是遙感圖像處理的重要應(yīng)用之一。遙感圖像變化檢測是通過對(duì)同一區(qū)域不同時(shí)期的兩幅已配準(zhǔn)的遙感圖像進(jìn)行分析,檢測出該區(qū)域地表變化信息的過程。變化檢測技術(shù)在自然災(zāi)害監(jiān)測、生態(tài)環(huán)境監(jiān)測、戰(zhàn)場動(dòng)態(tài)監(jiān)視等領(lǐng)域得到了廣泛的應(yīng)用。 本文主要研究了基于分?jǐn)?shù)階Fourier變換(Fractional Fourier Transform, FRFT)和Gabor小波的遙感圖像變化檢測算法,主要內(nèi)容如下所述： 1、本文將非平穩(wěn)信號(hào)處理理論的重要分支之一,分?jǐn)?shù)階Fourier變換應(yīng)用到遙感圖像變化檢測中,提出一種新的無監(jiān)督變化檢測算法。對(duì)同一地區(qū)不同時(shí)期獲得的多時(shí)相遙感圖像采取低階次的分?jǐn)?shù)階Fourier變換,然后根據(jù)圖像類型計(jì)算差異圖像。該算法結(jié)合每個(gè)像素的鄰域信息,并利用主成分分析(Principal Component Analysis, PCA)產(chǎn)生與每個(gè)像素對(duì)應(yīng)的基于鄰域信息的特征向量。變化區(qū)域檢測問題可以轉(zhuǎn)化為一個(gè)二分類問題,利用K-means算法將特征向量分為兩類：變化類和不變化類,得到變化檢測圖。最后使用光學(xué)遙感圖像和合成孔徑雷達(dá)(Synthetic Aperture Radar, SAR)圖像數(shù)據(jù)進(jìn)行仿真實(shí)驗(yàn),實(shí)驗(yàn)結(jié)果證實(shí)了本文提出算法的有效性。 2、本文提出一種基于Gabor小波和兩級(jí)聚類的多時(shí)相遙感圖像變化檢測算法,利用同一地區(qū)不同時(shí)期的多時(shí)相遙感圖像獲得差異圖像,然后得到差異圖像不同尺度和不同方向的Gabor小波變換,提取差異圖像中每一個(gè)像素多尺度和多方向數(shù)據(jù)組成特征向量。同時(shí)本文提出一種基于模糊C均值(Fuzzy C-means, FCM)聚類的兩級(jí)聚類算法,提高了變化檢測的效果。通過與幾種現(xiàn)有算法檢測結(jié)果的對(duì)比,可以看到本文提出的算法具有更好的檢測效果。 3、本文提出一種基于Gabor小波和PCA的多時(shí)相遙感圖像變化檢測算法,利用Gabor小波變換獲得差異圖像的多尺度和多方向數(shù)據(jù),但是為了減少計(jì)算的復(fù)雜度,我們使用PCA對(duì)多尺度和多方向Gabor特征數(shù)據(jù)進(jìn)行降維。本文分別使用了K-means和FCM兩種算法實(shí)現(xiàn)聚類獲得變化檢測結(jié)果,進(jìn)而得到兩種聚類算法的性能對(duì)比。最后使用光學(xué)遙感圖像和SAR圖像進(jìn)行仿真實(shí)驗(yàn),證實(shí)了本文提出算法的有效性。
[Abstract]:Change detection technology is one of the important applications of remote sensing image processing. Remote sensing image change detection is a process of detecting the surface change information of the same region by analyzing two registered remote sensing images in different periods of the same area. Change detection technology has been widely used in natural disaster monitoring, ecological environment monitoring, battlefield dynamic monitoring and other fields. In this paper, the algorithm of remote sensing image change detection based on fractional Fourier transform (Fractional Fourier Transform, FRFT) and Gabor wavelet is studied. The main contents are as follows: 1. One of the important branches of the theory of non-stationary signal processing is introduced in this paper. Fractional Fourier transform is applied to remote sensing image change detection, and a new unsupervised change detection algorithm is proposed. The multitemporal remote sensing images obtained from different periods in the same area were obtained by fractional Fourier transform of low order, and then the differential images were calculated according to the image types. The algorithm combines the neighborhood information of each pixel and uses principal component analysis (Principal Component Analysis, PCA) to generate feature vectors corresponding to each pixel based on neighborhood information. The problem of variable region detection can be transformed into a two-classification problem. The feature vector can be divided into two categories by using K-means algorithm: the change class and the invariant class, and the change detection graph can be obtained. Finally, the optical remote sensing image and synthetic aperture radar (Synthetic Aperture Radar, SAR) image data are used for simulation experiment. The experimental results prove the validity of the proposed algorithm. 2. This paper proposes a multi-temporal remote sensing image change detection algorithm based on Gabor wavelet and two-level clustering. The multitemporal remote sensing images of the same region are used to obtain the differential images, and then the Gabor wavelet transform with different scales and directions is obtained to extract the multi-scale and multi-direction data of each pixel in the differential image. At the same time, a two-level clustering algorithm based on Fuzzy C-means (FCM) clustering is proposed, which improves the effect of change detection. By comparing the detection results with several existing algorithms, we can see that the algorithm proposed in this paper has better detection effect. 3. This paper proposes a multi-temporal remote sensing image change detection algorithm based on Gabor wavelet and PCA. Gabor wavelet transform is used to obtain the multi-scale and multi-directional data of the differential image, but in order to reduce the computational complexity, we use PCA to reduce the dimension of the multi-scale and multi-directional Gabor feature data. In this paper, two algorithms, K-means and FCM, are used to achieve the change detection results, and then the performance of the two clustering algorithms is compared. Finally, the simulation results of optical remote sensing images and SAR images show that the proposed algorithm is effective.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP751

【參考文獻(xiàn)】

中國期刊全文數(shù)據(jù)庫 前3條

1 李金基;焦李成;張向榮;楊咚咚;;基于兩時(shí)相圖像聯(lián)合分類的SAR圖像變化檢測[J];紅外與毫米波學(xué)報(bào);2009年06期

2 馬國銳;李平湘;秦前清;;基于融合和廣義高斯模型的遙感影像變化檢測[J];遙感學(xué)報(bào);2006年06期

3 王桂婷;王幼亮;焦李成;;自適應(yīng)空間鄰域分析和瑞利-高斯分布的多時(shí)相遙感影像變化檢測[J];遙感學(xué)報(bào);2009年04期

，

本文編號(hào)：2263104