【摘要】：目前,高光譜遙感技術(shù)發(fā)展迅速,應(yīng)用范圍廣泛,為了獲取高質(zhì)量的影像數(shù)據(jù),在改善儀器設(shè)備有限的情況下,還可以通過影像處理的方式提高影像質(zhì)量。針對(duì)獲取影像數(shù)據(jù)時(shí)受到的噪聲影響問題,對(duì)高光譜影像數(shù)據(jù)進(jìn)行去噪處理便成為影像處理步驟中不可缺少的一步,所以本文提出了一種基于非平穩(wěn)表示模型的高光譜影像去噪方法。在高光譜影像中,對(duì)于某個(gè)單獨(dú)像元來說,從光譜維來看,它具有一條完整的光譜曲線,可以用于判別該像元所對(duì)應(yīng)的地表內(nèi)包含的地物類型;從空間維來看,可以獲取該像元在地面的位置信息,及與其它像元在空間的位置上的排列組合,這是高光譜影像所獨(dú)有的“圖譜合一”特點(diǎn)。而且由于傳感器的空間分辨率較低,使得每個(gè)像元所對(duì)應(yīng)的地面范圍較廣,包含的地物較多,其像元的光譜曲線是多種地物的光譜曲線的混合。為了解決這一問題,利用高光譜影像數(shù)據(jù)獨(dú)有的特點(diǎn)進(jìn)行混合像元分解,得到“真實(shí)的”端元值和豐度值,再進(jìn)行影像重建便可以得到去噪后的高光譜影像。這是基于混合光譜分解的去噪方法。在本文中所提出的非平穩(wěn)表示方法是在基于混合光譜分解的去噪方法基礎(chǔ)上更進(jìn)一步的研究,主要內(nèi)容有:1、根據(jù)線性光譜混合模型,將這些像素及其權(quán)重用于本征表示解混方法中,以解混的思想對(duì)中心像元進(jìn)行混合像元分解,將求解得到的“真實(shí)的”端元值和豐度值進(jìn)行影像重建,恢復(fù)之后的高光譜影像,即是得到的去噪后影像。2、針對(duì)高光譜影像數(shù)據(jù)具有空間非平穩(wěn)性,依托由Fuentes等人提出的空間非平穩(wěn)建模方法;利用非局部均值方法尋找與中心像元相似度大的像素,分別使用馬氏距離和歐氏距離計(jì)算圖像塊之間的相似度作為權(quán)重。3、利用模擬高光譜數(shù)據(jù)和真實(shí)高光譜數(shù)據(jù)進(jìn)行實(shí)驗(yàn),對(duì)去噪前后的影像進(jìn)行定性和定量評(píng)價(jià)。分別計(jì)算模擬影像的峰值信噪比,結(jié)構(gòu)相似度,均方根誤差和真實(shí)影像的信噪比。經(jīng)過實(shí)驗(yàn)結(jié)果證明,本文模型能夠很好的同時(shí)在光譜維和空間維對(duì)高光譜影像去噪,相比較其它方法,本文中所提出的方法具有很好的穩(wěn)健性,能更多的保留影像的空間紋理信息。
[Abstract]:At present, hyperspectral remote sensing technology has been developed rapidly and widely used. In order to obtain high quality image data, the image quality can be improved by image processing under the condition of limited instrument and equipment. In order to solve the problem of noise impact on the acquisition of image data, de-noising of hyperspectral image data becomes an indispensable step in image processing. In this paper, a method of hyperspectral image denoising based on nonstationary representation model is proposed. In hyperspectral images, for a single pixel, it has a complete spectral curve from the perspective of spectral dimension, which can be used to distinguish the type of ground objects contained in the surface corresponding to the pixel. From the spatial dimension, the location information of the pixel on the ground and its arrangement and combination with other pixels in space can be obtained, which is the unique feature of hyperspectral images. Because of the low spatial resolution of the sensor, each pixel corresponds to a wide range of ground and contains more ground objects. The spectral curve of each pixel is a mixture of the spectral curves of a variety of ground objects. In order to solve this problem, using the unique characteristics of hyperspectral image data to decompose the mixed pixel, get the "real" end element value and abundance value, and then reconstruct the image to get the de-noised hyperspectral image. This is a denoising method based on mixed spectral decomposition. The non-stationary representation method proposed in this paper is further studied on the basis of the noise removal method based on the mixed spectral decomposition. The main contents are as follows: 1. According to the linear spectral mixing model, These pixels and their weights are used in the intrinsic representation demultiplexing method, the central pixel is decomposed with the idea of unmixing, the "real" end element value and the abundance value are reconstructed, and the hyperspectral image is restored. That is, the de-noised image. 2. Aiming at the spatial nonstationarity of hyperspectral image data, this paper relies on the spatial non-stationary modeling method proposed by Fuentes et al. The nonlocal mean method is used to find pixels with high similarity to the center pixel, and the similarity between the blocks is calculated by Markov distance and Euclidean distance respectively. 3. Experiments are carried out using simulated hyperspectral data and real hyperspectral data. The images before and after denoising were evaluated qualitatively and quantitatively. The peak signal-to-noise ratio (PSNR), structural similarity, root mean square error (RMS) and signal-to-noise ratio (SNR) of real images are calculated respectively. The experimental results show that the proposed method is robust to the hyperspectral image denoising at the same time in spectral dimension and spatial dimension. Compared with other methods, the proposed method in this paper is robust. Can retain more spatial texture information of the image.
【學(xué)位授予單位】：中國(guó)地質(zhì)大學(xué)(北京)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：P237

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 陳晉;馬磊;陳學(xué)泓;饒玉晗;;混合像元分解技術(shù)及其進(jìn)展[J];遙感學(xué)報(bào);2016年05期

2 張兵;;高光譜圖像處理與信息提取前沿[J];遙感學(xué)報(bào);2016年05期

3 童慶禧;張兵;張立福;;中國(guó)高光譜遙感的前沿進(jìn)展[J];遙感學(xué)報(bào);2016年05期

4 王合玲;張輝國(guó);呂光輝;;艾比湖流域土壤有機(jī)質(zhì)與土壤因子響應(yīng)關(guān)系的空間非平穩(wěn)性分析[J];水土保持研究;2016年02期

5 馬文武;侯妙樂;胡云崗;;基于地面高光譜遙感的石碑特征信息提取[J];北京建筑大學(xué)學(xué)報(bào);2015年02期

6 陶中平;胡先莉;王茂芝;張小東;程賓洋;;高光譜巖心數(shù)據(jù)管理及其分析[J];地質(zhì)力學(xué)學(xué)報(bào);2015年02期

7 印佳;杜戰(zhàn)戰(zhàn);;基于主成分分析的高光譜遙感圖像非局部去噪[J];現(xiàn)代電子技術(shù);2015年11期

8 吳建峰;黃樹彩;康紅霞;凌強(qiáng);吳瀟;;高光譜成像技術(shù)在天基導(dǎo)彈預(yù)警探測(cè)中的應(yīng)用[J];飛航導(dǎo)彈;2015年02期

9 李美凌;鄧飛;劉穎;祁亨年;張曉;;基于高光譜圖像的水稻種子活力檢測(cè)技術(shù)研究[J];浙江農(nóng)業(yè)學(xué)報(bào);2015年01期

10 徐冬;孫蕾;羅建書;;結(jié)合NAPCA和復(fù)小波變換的高光譜遙感圖像去噪[J];紅外與激光工程;2015年01期

相關(guān)博士學(xué)位論文 前3條

1 曾志高;概率模型及其在視頻對(duì)象處理中的應(yīng)用[D];武漢大學(xué);2012年

2 孫偉峰;基于非局部信息的信號(hào)與圖像處理算法及其應(yīng)用研究[D];山東大學(xué);2010年

3 曾智勇;基于內(nèi)容圖像數(shù)據(jù)庫(kù)檢索中的關(guān)鍵技術(shù)研究[D];西安電子科技大學(xué);2006年

相關(guān)碩士學(xué)位論文 前3條

1 蔡茂知;基于非線性混合模型的高光譜波譜信號(hào)提取方法研究[D];電子科技大學(xué);2016年

2 張?zhí)煊?基于高光譜遙感的黃土覆蓋區(qū)油氣微滲漏信息提取[D];吉林大學(xué);2015年

3 段海軍;基于Markov隨機(jī)場(chǎng)的上下文圖像分類與紋理分割研究[D];昆明理工大學(xué);2013年

，

本文編號(hào)：2409111