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  本文選題：高光譜圖像 + 信息恢復(fù)��； 參考：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：高光譜圖像的數(shù)據(jù)有著豐富的空間信息和光譜信息,能夠更好的反映地物的實際情況,在民用和軍事領(lǐng)域有著巨大的應(yīng)用前景。高光譜圖像容易受到外界因素的影響,出現(xiàn)圖像質(zhì)量下降,信息丟失的情況,對圖像的后續(xù)處理帶來了一定的問題,所以圖像的預(yù)處理尤為重要。高光譜圖像的信息恢復(fù)技術(shù)作為高光譜圖像的預(yù)處理過程用到的技術(shù),一直是遙感領(lǐng)域研究的熱點(diǎn)問題之一。其中,條帶去除和超分辨重建是高光譜圖像信息恢復(fù)中兩個重要的問題。由于諸多因素的影響,高光譜圖像在獲取和傳輸?shù)倪^程中,容易產(chǎn)生條帶狀的噪聲,使其丟失了大量的重要信息,為圖像接下來的處理帶來了巨大的阻礙。因此,對高光譜圖像進(jìn)行條帶去除是其預(yù)處理中的較為重要的一步。對于條帶去除而言,現(xiàn)有的方法在處理條帶缺失列數(shù)較多、地物較為復(fù)雜時,取得的條帶去除效果較差。而基于深度學(xué)習(xí)的卷積神經(jīng)網(wǎng)絡(luò)具有良好的邊緣特征學(xué)習(xí)能力和挖掘海量信息背后隱藏的信息的能力,卷積神經(jīng)網(wǎng)絡(luò)作為時下較為流行的機(jī)器學(xué)習(xí)技術(shù),在圖像處理領(lǐng)域已有著廣泛應(yīng)用。本篇論文就把深度卷積神經(jīng)網(wǎng)絡(luò)運(yùn)用到條帶去除中來,實驗表明,當(dāng)缺失條帶列數(shù)較多時,該方法能取得比傳統(tǒng)方法更好的條帶去除效果。高光譜圖像具有較高的光譜分辨率和較低的空間分辨率,較低的空間分辨率大大限制了高光譜圖像的實際應(yīng)用。從硬件上來提高高光譜圖像的分辨率代價較高,于是本篇論文把超分辨率重建的方法運(yùn)用到提高高光譜圖像的空間分辨率和光譜分辨率中來。在超分辨率重建中,基于深度卷積神經(jīng)網(wǎng)絡(luò)的方法能夠?qū)崿F(xiàn)端對端的重建,是時下較為成功的重建方法。本文針對高光譜圖像的特點(diǎn),構(gòu)建一維、二維和三維的卷積神經(jīng)網(wǎng)絡(luò)來分別恢復(fù)高光譜圖像的光譜信息、空間信息和空-譜信息。實驗結(jié)果表明,基于深度卷積神經(jīng)網(wǎng)絡(luò)的超分辨率重建比傳統(tǒng)的方法能夠更好地恢復(fù)高光譜圖像的光譜和空間信息,尤其是空間信息,使得該方法有著巨大的實際應(yīng)用前景。
[Abstract]:The data of hyperspectral images have abundant spatial and spectral information, which can better reflect the actual situation of ground objects, and have great application prospects in civil and military fields. Hyperspectral image is easy to be affected by external factors, the image quality decline, information loss, which brings some problems to the subsequent processing of the image, so the image preprocessing is particularly important. As a preprocessing technology of hyperspectral image, information restoration technology of hyperspectral image has been one of the hot issues in remote sensing field. Strip removal and super-resolution reconstruction are two important problems in hyperspectral image information restoration. Because of the influence of many factors, the hyperspectral image is easy to produce banded noise in the process of acquisition and transmission, resulting in the loss of a large number of important information, which brings great obstacles to the next processing of the image. Therefore, the strip removal of hyperspectral images is an important step in the preprocessing of hyperspectral images. For strip removal, the existing methods are less effective when the number of missing strips is more and the features are more complex. The convolution neural network based on deep learning has good edge feature learning ability and the ability to mine the hidden information behind massive information. Convolution neural network is a popular machine learning technology. It has been widely used in the field of image processing. In this paper, the deep convolution neural network is applied to strip removal. Experiments show that this method can achieve better strip removal effect than the traditional method when the number of missing bands is more. Hyperspectral images have higher spectral resolution and lower spatial resolution, and the lower spatial resolution greatly limits the practical application of hyperspectral images. It is very expensive to improve the resolution of hyperspectral image by hardware, so this paper applies the method of super-resolution reconstruction to improve the spatial resolution and spectral resolution of hyperspectral image. In super-resolution reconstruction, the method based on deep convolution neural network can realize end-to-end reconstruction, which is a successful reconstruction method. According to the characteristics of hyperspectral images, a convolution neural network of one dimension, two dimensions and three dimensions is constructed to recover the spectral information, spatial information and space-spectrum information of hyperspectral images respectively. The experimental results show that the super-resolution reconstruction based on deep convolution neural network can recover the spectral and spatial information of hyperspectral images better than the traditional method.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;TP183

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本文編號：1956484