本文選題：圖像配準(zhǔn) + 超分辨率重建��； 參考：《哈爾濱師范大學(xué)》2014年碩士論文

【摘要】：1972年，美國發(fā)射了首顆地球觀測衛(wèi)星—landsat-1，標(biāo)志著人類進(jìn)入了遙感時代的新紀(jì)元。隨著遙感技術(shù)的迅猛發(fā)展和廣泛應(yīng)用，雖然遙感圖像的分辨率已經(jīng)不斷提高但還是難以滿足許多領(lǐng)域?qū)τ诟叻直媛蔬b感影像的需求，比如在遙感圖像小目標(biāo)（飛機(jī)、船只、橋梁等）識別當(dāng)中，需要尋找的目標(biāo)通常都是米級目標(biāo)，利用現(xiàn)有的遙感圖像，進(jìn)行小目標(biāo)分割精度偏低。提高遙感圖像的分辨率可以通過改良硬件設(shè)備實現(xiàn)，但工藝水平越復(fù)雜精細(xì)，成本也會越高昂，并且成像設(shè)備改良的空間也是有極限的，人們開始嘗試?yán)密浖姆椒ń鉀Q這一問題，超分辨率重建技術(shù)在這個背景下應(yīng)運而生。 本文利用超分辨率重建技術(shù)提高現(xiàn)有遙感圖像的分辨率，提出了一種基于的超分辨率重建方法，為目標(biāo)分割提供包含更豐富有效特征的遙感圖像源數(shù)據(jù)，將超分辨率重建技術(shù)運用于小目標(biāo)識別，并通過實驗成功提出小目標(biāo)（塘壩）。 本文對超分辨率重建技術(shù)關(guān)鍵技術(shù)做了較為深入的研究，主要包括以下幾個方面： 遙感圖像配準(zhǔn)，高精度的圖像配準(zhǔn)是完成超分辨率重建的基礎(chǔ)，本文介紹了現(xiàn)在常用的圖像配準(zhǔn)技術(shù)，并分析了現(xiàn)有遙感配準(zhǔn)技術(shù)的難點問題，針對小目標(biāo)遙感圖像的特殊性采用一種基于SIFT的多光譜遙感圖像配準(zhǔn)方法，通過實驗驗證本文方法可以快速、自動的完成高精度配準(zhǔn)。 遙感圖像超分辨率重構(gòu)，目前序列超分辨率算法主要分為頻域類和空域類兩類方法。本文概述了兩類方法的主要算法原理和優(yōu)點，并提出一種基于Hopfield神經(jīng)網(wǎng)絡(luò)的超分辨重建算法，在遙感圖像超分辨率重建的結(jié)果中，往往圖像的邊界等細(xì)節(jié)容易產(chǎn)生模糊，而這些部分又包含較多重要的信息，該方法可以有效提高細(xì)節(jié)保護(hù)的能力，通過實驗取得較好的超分辨效果。 將超分辨率技術(shù)運用在小目標(biāo)分割中，小目標(biāo)分割的精度不僅依賴于目標(biāo)分割方法，高分辨率的遙感圖像作為源數(shù)據(jù)也是重要因素之一，高分辨率的遙感圖像能夠為目標(biāo)識別提供更多有效特征，提高識別精度。本文將超分辨率技術(shù)運用于目標(biāo)分割中，，通過實驗完了對小目標(biāo)（塘壩）的目標(biāo)識別。
[Abstract]:In 1972, the United States launched its first Earth observation satellite-Landsat-1, marking a new era in the era of remote sensing. With the rapid development and wide application of remote sensing technology, although the resolution of remote sensing images has been continuously improved, it is still difficult to meet the needs of high-resolution remote sensing images in many fields, such as small targets (aircraft, ships) in remote sensing images. In the recognition of bridges, the targets that need to be looked for are usually meter targets. The segmentation accuracy of small targets is low by using the existing remote sensing images. Improving the resolution of remote sensing images can be achieved through improved hardware, but the more sophisticated the process, the higher the cost, and there is a limit to the space for improved imaging equipment. People began to use software to solve this problem, and super-resolution reconstruction technology came into being under this background. In this paper, the super-resolution reconstruction technique is used to improve the resolution of the existing remote sensing images, and a super-resolution reconstruction method based on super-resolution is proposed, which provides the source data of remote sensing images with more effective features for target segmentation. The super-resolution reconstruction technique is applied to small target recognition, and the small target (Tangba) is proposed successfully by experiment. In this paper, the key technologies of super-resolution reconstruction are studied, including the following aspects: Remote sensing image registration and high-precision image registration are the basis of super-resolution reconstruction. This paper introduces the common image registration technology and analyzes the difficult problems of the existing remote sensing registration technology. According to the particularity of small target remote sensing image, a multi-spectral remote sensing image registration method based on SIFT is adopted. The experimental results show that this method can achieve high precision registration quickly and automatically. In remote sensing image super-resolution reconstruction, the sequence super-resolution algorithms are mainly divided into two categories: frequency domain and spatial domain. In this paper, the principle and advantages of the two kinds of methods are summarized, and a super-resolution reconstruction algorithm based on Hopfield neural network is proposed. In the super-resolution reconstruction of remote sensing image, the details such as the edge of the image are often blurred. These parts contain more important information. This method can effectively improve the ability of detail protection and obtain better super-resolution effect through experiments. The super-resolution technology is applied to small target segmentation. The accuracy of small target segmentation depends not only on the method of target segmentation, but also on the high resolution remote sensing image as one of the important factors. High resolution remote sensing images can provide more effective features for target recognition and improve recognition accuracy. In this paper, the super-resolution technique is applied to target segmentation, and the target recognition of small target (Tangba) is completed through experiments.
【學(xué)位授予單位】：哈爾濱師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP751

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本文編號：1850774