本文選題：遙感圖像 + 數(shù)學(xué)形態(tài)學(xué)��； 參考：《成都理工大學(xué)》2014年碩士論文

【摘要】：在圖像處理過程中,人們往往對圖像中某些部分感興趣,而不同的目標(biāo)在圖像中一般具有不同的特征。為了識別和分析圖像中的目標(biāo)區(qū)域,就需要把它們從圖像中分離開來。遙感圖像通常分辨率較高,包含信息豐富,目標(biāo)結(jié)構(gòu)比較復(fù)雜,同時也含有大量的噪聲。由于遙感圖像的這些特性,使得傳統(tǒng)數(shù)學(xué)形態(tài)學(xué)的圖像分割算法并不能完全適用,因此在一定程度上阻礙了圖像分割技術(shù)在遙感領(lǐng)域的推廣和應(yīng)用。 本文針對遙感圖像的特性,結(jié)合數(shù)學(xué)形態(tài)學(xué)在圖像分割中的應(yīng)用,提出了一種基于數(shù)學(xué)形態(tài)學(xué)的改進(jìn)分水嶺分割算法。通過實驗驗證,該算法具有高效、準(zhǔn)確、快速等特點,可以得到連續(xù)封閉的目標(biāo)區(qū)域。由于傳統(tǒng)分水嶺分割算法對噪聲比較敏感,將該算法直接作用在遙感圖像中會產(chǎn)生過分割現(xiàn)象,分割的效果并不理想。本文的主要工作包含以下幾個方面： 首先,本文以二值形態(tài)學(xué)為基礎(chǔ),介紹了數(shù)學(xué)形態(tài)學(xué)的膨脹和腐蝕基本運(yùn)算,并擴(kuò)展到灰度形態(tài)學(xué)當(dāng)中。通過幾種梯度算子的分析和比較,本文提出的多尺度形態(tài)學(xué)梯度算子能夠得到較為理想的梯度圖像。 其次,采用擴(kuò)展最小變換對梯度圖像進(jìn)行標(biāo)記時,閾值的選取通常是人工設(shè)定的,一般帶有一定的盲目性。本文采用二維Otsu閾值分割算法自適應(yīng)地獲取最佳閾值,避免了人為的干預(yù)。實驗表明,該算法可以有效地抑制噪聲的干擾,能夠標(biāo)記出圖像的主要輪廓,保持完整的信息。 最后,本文提出標(biāo)記分水嶺分割算法,克服傳統(tǒng)分水嶺算法存在的過分割缺陷。對輸入的原始圖像進(jìn)行形態(tài)學(xué)濾波處理,在得到的梯度圖像上提取標(biāo)記,并把標(biāo)記強(qiáng)制作為極小值修改梯度圖像。從實驗的結(jié)果可以看到,該方法有效地解決了分水嶺算法的過分割問題,得到了較好的分割效果。
[Abstract]:In the process of image processing, people are often interested in some parts of the image, and the different targets usually have different features in the image. In order to identify and analyze the target area in the image, it is necessary to separate them from the image. The remote sensing image usually has a high resolution, contains rich information, and the structure of the target is more complex. It also contains a lot of noise. Because of the characteristics of remote sensing images, the traditional mathematical morphology image segmentation algorithm can not be fully applied, so to a certain extent, it hinders the popularization and application of image segmentation technology in the field of remote sensing.
In view of the characteristics of remote sensing images and the application of mathematical morphology in image segmentation, an improved watershed segmentation algorithm based on mathematical morphology is proposed. Through experiments, it is proved that the algorithm has the characteristics of high efficiency, accuracy and fast speed and so on. It can get the continuous closed target area. Because the traditional watershed segmentation algorithm has the noise ratio to the noise ratio. It is more sensitive to use the algorithm directly in remote sensing image to produce over segmentation, and the effect of segmentation is not ideal. The main work of this paper includes the following aspects:
First, based on the two value morphology, this paper introduces the basic operation of the expansion and corrosion of mathematical morphology and extends to the gray scale morphology. Through the analysis and comparison of several gradient operators, the multi-scale morphological gradient operator proposed in this paper can get a more ideal gradient image.
Secondly, when using the extended minimum transform to mark the gradient image, the selection of threshold is usually artificial, and it usually has a certain blindness. In this paper, a two-dimensional Otsu threshold segmentation algorithm is used to obtain the best threshold and avoid human interference. The experiment shows that the algorithm can effectively suppress the noise interference and can be labeled. Remember the main outline of the image and keep the complete information.
Finally, this paper proposes a marked watershed segmentation algorithm to overcome the oversegmentation defects in the traditional watershed algorithm. The original image is processed by morphological filtering, and the labeling is extracted on the obtained gradient image, and the mark is forced to modify the gradient image as a minimum. It can be seen from the experimental results that the method is effectively solved. The over segmentation problem of watershed algorithm is proved to be effective.

【學(xué)位授予單位】：成都理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP751

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