【摘要】：隨著空間遙感技術(shù)的快速發(fā)展,遙感圖像已經(jīng)被廣泛應(yīng)用到各行各業(yè)。由于遙感圖像內(nèi)容包含的范圍廣、數(shù)據(jù)大,各部們所關(guān)心的內(nèi)容不同,所以如何將遙感圖像中的各個(gè)要素準(zhǔn)確的分類和提取已經(jīng)成為目前研究的熱點(diǎn)。學(xué)者們常用的分類方法主要包括最大似然法、最小距離法、支持向量機(jī)法等,但是這些方法都沒有充分的考慮各像元間的位置關(guān)系,以導(dǎo)致“同物異譜”和“異物同譜”的現(xiàn)象出現(xiàn),使得最后的分類準(zhǔn)確度不高。然而均值漂移算法是一種基于核密度估計(jì)的非參數(shù)核密度估計(jì)算法,不依賴參數(shù)的估計(jì)以及概率密度函數(shù)的選擇。均值漂移算法具有運(yùn)算量小、易于實(shí)現(xiàn)等優(yōu)勢(shì),在圖像平滑,圖像分割以和目標(biāo)的跟蹤等方面已經(jīng)得到了廣泛應(yīng)用。因此本文根據(jù)均值漂移算法的聚類特性,給出了兩種遙感圖像分類的改進(jìn)方法。論文主要工作及研究成果概括如下：(1)針對(duì)均值漂移算法的聚類特性,以遙感圖像為例,討論了該算法核函數(shù)中的兩個(gè)參數(shù)對(duì)圖像分割的影響。(2)本文利給出了兩種改進(jìn)的遙感圖像分類方法,一種是均值漂移算法與支持向量機(jī)結(jié)合的分類方法,另一種是均值漂移算法與最小距離結(jié)合的方法。并從三個(gè)方面與常用分類方法進(jìn)行對(duì)比：kappa系數(shù)、混淆矩陣以及分類時(shí)間。實(shí)驗(yàn)結(jié)果表明,本文給出的兩種改進(jìn)方法分別在效果與時(shí)間上具有顯著的優(yōu)勢(shì)。(3)本文給出了一種改進(jìn)的遙感圖像要素提取方法,該方法通過引入等高線、二值化、形態(tài)學(xué)等方法進(jìn)行改進(jìn)。該方法在提取河流的效果與運(yùn)算的時(shí)間上都有很好的表現(xiàn)。
[Abstract]:With the rapid development of space remote sensing technology, remote sensing images have been widely used in various industries. Because of the wide range of remote sensing image content, large data, different content concerned by various departments, how to accurately classify and extract each element of remote sensing image has become a hot research topic at present. The commonly used classification methods include the maximum likelihood method, the minimum distance method, the support vector machine method and so on. However, these methods do not fully consider the location relationship among the pixels. As a result of the phenomenon of "isomorphism" and "isomorphism of foreign bodies", the classification accuracy of the final classification is not high. However, the mean shift algorithm is a nonparametric kernel density estimation algorithm based on kernel density estimation, which does not depend on the estimation of parameters and the selection of probability density function. Mean shift algorithm has many advantages, such as low computation and easy to be realized. It has been widely used in image smoothing, image segmentation and target tracking. Therefore, according to the clustering characteristics of the mean shift algorithm, two improved methods of remote sensing image classification are presented in this paper. The main work and research results are summarized as follows: (1) according to the clustering characteristics of mean shift algorithm, remote sensing image is taken as an example. The effect of two parameters in kernel function on image segmentation is discussed. (2) two improved remote sensing image classification methods are presented in this paper, one is the combination of mean shift algorithm and support vector machine. The other is the combination of mean shift algorithm and minimum distance. And compared with common classification methods from three aspects: kappa coefficient, confusion matrix and classification time. The experimental results show that the two improved methods in this paper have significant advantages in effect and time respectively. (3) an improved method for extracting elements of remote sensing image is presented in this paper. Morphological methods were improved. This method has a good performance in the extraction of rivers and the time of operation.
【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】：2015
【分類號(hào)】：TP751

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 周家香;朱建軍;趙群河;;集成改進(jìn)Mean Shift和區(qū)域合并兩種算法的圖像分割[J];測(cè)繪科學(xué);2012年06期

，

本文編號(hào)：2337894