【摘要】：遙感圖像變化檢測(cè)是通過(guò)分析處理不同時(shí)期同一地點(diǎn)獲取的遙感圖像而獲取變化信息的技術(shù)。作為遙感圖像解譯的主要技術(shù)之一，遙感圖像變化檢測(cè)已經(jīng)廣泛應(yīng)用于國(guó)防軍情監(jiān)控、資源探測(cè)、環(huán)境監(jiān)測(cè)、城市規(guī)劃等領(lǐng)域。多光譜遙感圖像數(shù)據(jù)具有從可見(jiàn)光到紅外光波段的多個(gè)接收頻段，豐富的光譜信息增加了識(shí)別多種類型變化的可能性和可信度，因此多光譜遙感圖像在變化檢測(cè)中的應(yīng)用越來(lái)越廣泛。本文主要研究了多光譜遙感圖像的變化檢測(cè)，完成了如下兩方面的工作： （1）提出了一種基于Treelet融合和圖正則約束譜聚類的多光譜遙感圖像變化檢測(cè)方法。該方法首先對(duì)預(yù)處理后的兩時(shí)相各個(gè)波段圖像對(duì)應(yīng)做差，得到各個(gè)波段的差異圖，用非抽樣離散小波分解各個(gè)波段差異圖，得到各自的小波分解系數(shù)，然后將處在同一分解波段同一分解方向的所有波段的小波系數(shù)使用Treelet方法進(jìn)行融合，使用逆非抽樣離散小波重構(gòu)融合后得到的小波系數(shù)，得到新的差異圖。使用圖正則約束譜聚類分割新的差異圖，得到最終的變化檢測(cè)結(jié)果。我們通過(guò)對(duì)3組多光譜遙感圖像進(jìn)行實(shí)驗(yàn)，驗(yàn)證了本方法的有效性。 （2）提出了一種基于半監(jiān)督降維和顯著圖的多光譜遙感圖像變化檢測(cè)方法。該方法首先對(duì)預(yù)處理后的兩時(shí)相多波段圖像對(duì)應(yīng)波段做差，得到差異圖組，接著采用半監(jiān)督降維的方式將差異圖組融合為一幅新的差異圖。然后對(duì)得到的新的差異圖作兩種處理，，一種處理是直接對(duì)新的差異圖用K-means聚類，得到差異圖的類別標(biāo)記圖，另一種處理是對(duì)新的差異圖先提取顯著圖，對(duì)顯著圖用K-means聚類，得到顯著圖的類別標(biāo)記圖，將差異圖的類別標(biāo)記圖和顯著圖的類別標(biāo)記圖借助數(shù)學(xué)形態(tài)學(xué)的理論進(jìn)行融合，得到最終的變化檢測(cè)結(jié)果。我們通過(guò)3組多光譜遙感圖像進(jìn)行實(shí)驗(yàn)，驗(yàn)證了本方法的有效性。 本論文工作得到了國(guó)家自然科學(xué)基金（60970066）、國(guó)家自然科學(xué)基金（61173092）以及中央高�；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金（K50510020025）的資助。
[Abstract]:Remote sensing image change detection is a technique to obtain change information by analyzing and processing remote sensing images obtained at the same time and the same place. As one of the main techniques of remote sensing image interpretation, remote sensing image change detection has been widely used in the field of defense monitoring, resource detection, environmental monitoring, urban planning and so on. Multispectral remote sensing image data have multiple receiving bands from visible to infrared light bands, and rich spectral information increases the possibility and reliability of identifying various types of changes. Therefore, multispectral remote sensing images are more and more widely used in change detection. In this paper, the change detection of multispectral remote sensing images is studied, and the following two aspects are accomplished: (1) A change detection method for multispectral remote sensing images based on Treelet fusion and regular constrained spectral clustering is proposed. In this method, first of all, the 02:00 phase image of each band after preprocessing is mismatched, and the difference map of each band is obtained, and the wavelet decomposition coefficient is obtained by decomposing the difference image of each band by using non-sampling discrete wavelet transform. Then all the wavelet coefficients in the same decomposition band and the same decomposition direction are fused using Treelet method. The wavelet coefficients obtained from the fusion are reconstructed by inverse non-sampling discrete wavelets, and a new difference graph is obtained. The new difference graph is segmented by regular constraint spectrum clustering, and the final change detection result is obtained. Experiments on three sets of multispectral remote sensing images show that the proposed method is effective. (2) A multispectral remote sensing image change detection method based on semi-supervised reduction and saliency map is proposed. In this method, the 02:00 multiband images after preprocessing are first divided into the corresponding bands, and the difference map group is obtained, and then the difference map group is fused into a new difference map by semi-supervised dimensionality reduction. Then two kinds of processing are made for the new difference map, one is to cluster the new difference map directly with K-means, and the other is to extract the salient map from the new difference map. In this paper, K-means clustering is used to obtain the class marker map of the significant map, and the category marker map of the difference map and the class marker map of the salient map are fused with the theory of mathematical morphology, and the final change detection results are obtained. Three sets of multispectral remote sensing images are used to verify the effectiveness of this method. This thesis is supported by the National Natural Science Foundation of China (60970066), the National Natural Science Foundation of China (61173092) and the Special Fund for basic Scientific Research operating expenses of the Central University (K50510020025).
【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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