【摘要】：極化合成孔徑雷達(dá)(PolSAR)通過(guò)極化組合天線(xiàn)發(fā)射和接收電磁波,從接收電磁波中可以獲取地物豐富的散射信息。相比于單極化或者雙極化的SAR系統(tǒng),PolSAR系統(tǒng)能夠測(cè)量地物目標(biāo)更多的信息,也能更真實(shí)的反映地物目標(biāo)的情況。隨著PolSAR圖像分辨率的提高,雖然高分辨率圖像可以提供更多的紋理信息、結(jié)構(gòu)信息,但是PolSAR圖像的逐像素解譯隨之出現(xiàn)了一些問(wèn)題:PolSAR圖像中具有嚴(yán)重的相干斑噪聲,這就使得圖像中很多像素點(diǎn)是類(lèi)別不確定的噪聲點(diǎn),采用逐像素的方法對(duì)其進(jìn)行解譯,就會(huì)造成噪聲點(diǎn)被錯(cuò)誤處理;在高分辨率PolSAR圖像中,相鄰像素很有可能屬于同一種地物,因此這些像素中含有的信息基本相似,如果對(duì)這些像素單獨(dú)處理,則會(huì)造成信息冗余。為了解決逐像素解譯高分辨率PolSAR圖像帶來(lái)的一些問(wèn)題,有學(xué)者提出了超像素的概念。超像素,是指圖像中在形狀、顏色、紋理等方面相似度很高的像素點(diǎn)組成的均勻圖像塊。在高分辨率圖像中,數(shù)據(jù)量很大,像素?cái)?shù)有成千上萬(wàn)個(gè),經(jīng)過(guò)超像素分割,后續(xù)就變?yōu)閷?duì)幾百個(gè)超像素處理。超像素的概念是在光學(xué)圖像中提出的,本文根據(jù)PolSAR圖像的極化特性,將超像素分割應(yīng)用在PolSAR圖像中,主要研究?jī)?nèi)容有以下三個(gè)方面:首先,研究了PolSAR的基礎(chǔ)理論和極化數(shù)據(jù)的表征方法,研究了多個(gè)表征方式之間的相互轉(zhuǎn)換關(guān)系,為了從PolSAR圖像中提取豐富的極化信息和紋理信息,研究了PolSAR圖像的目標(biāo)分解方法和常用的紋理特征提取方法。其次,研究了在PolSAR圖像中的相異性計(jì)算方法,根據(jù)提取的極化特征和紋理特征,提出利用特征之間的歐式距離計(jì)算相異性的思想;研究了超像素分割的基本原理,以及傳統(tǒng)的SLIC算法和傳統(tǒng)的分水嶺算法;結(jié)合PolSAR圖像的特性,對(duì)這兩種算法進(jìn)行改進(jìn),使其在PolSAR圖像中能夠獲取邊緣貼合度高的超像素。最后,研究了超像素分割結(jié)果的評(píng)價(jià)準(zhǔn)則,給出了適合PolSAR圖像的評(píng)價(jià)準(zhǔn)則,并利用三幅實(shí)驗(yàn)數(shù)據(jù)進(jìn)行實(shí)驗(yàn)驗(yàn)證,并對(duì)結(jié)果進(jìn)行定性和定量評(píng)價(jià)。
[Abstract]:Polarimetric synthetic Aperture Radar (PolSAR) transmits and receives electromagnetic waves through polarimetric combined antennas, which can obtain abundant scattering information from the received electromagnetic waves. Compared with SAR system with single or double polarization, PolSAR system can measure more information of ground object and reflect the situation of ground object more realistically. With the improvement of the resolution of PolSAR images, although high-resolution images can provide more texture information and structure information, there are some problems in the pixel by pixel interpretation of PolSAR images: there is serious speckle noise in PolSAR images. As a result, many pixels in the image are noise points with uncertain categories, which can be interpreted by the method of pixel by pixel. In high-resolution PolSAR images, adjacent pixels are likely to belong to the same ground object. Therefore, the information contained in these pixels is basically similar, if these pixels are processed separately, the information will be redundant. In order to solve some problems caused by pixel interpretation of high resolution PolSAR images, some scholars have proposed the concept of super pixel. Super pixel is a uniform image block composed of pixels with high similarity in shape, color, texture and so on. In high-resolution images, the amount of data is very large, there are tens of thousands of pixels, after super-pixel segmentation, the subsequent processing becomes hundreds of super-pixels. The concept of super-pixel is proposed in optical image. According to the polarization characteristics of PolSAR image, this paper applies hyperpixel segmentation to PolSAR image. The main research contents are as follows: first, In order to extract rich polarization and texture information from PolSAR images, the basic theory of PolSAR and the representation method of polarization data are studied. The target decomposition and texture feature extraction methods of PolSAR images are studied. Secondly, the different computing method in PolSAR image is studied. According to the extracted polarization feature and texture feature, the idea of using the Euclidean distance between the features to calculate the difference is put forward, and the basic principle of super-pixel segmentation is studied. And the traditional SLIC algorithm and the traditional watershed algorithm, combined with the characteristics of the PolSAR image, the two algorithms are improved to obtain the super-pixel with high edge fit in the PolSAR image. Finally, the evaluation criteria of the super-pixel segmentation results are studied, and the evaluation criteria suitable for PolSAR images are given. The experimental results are verified by using three experimental data, and the results are evaluated qualitatively and quantitatively.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TN957.52

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