【摘要】：ETM+遙感器對(duì)地觀測(cè)獲取的遙感圖像，存在大氣干擾高頻分量、前期圖像處理留下的殘余誤差及來(lái)源不明的其它誤差，在實(shí)際應(yīng)用中需要去噪處理。為了提高圖像清晰度，學(xué)者們探索了多種方法，其中低通濾波效果較好，被遙感圖像處理軟件廣泛采用，但濾波效果仍不理想，地物內(nèi)部弱顯清晰，地物邊沿弱顯糊化，弱化噪聲同時(shí)弱化了信號(hào)。去噪方法尚待改進(jìn)，新方法尚待探索，希望新方法能弱化噪聲、增強(qiáng)信號(hào)、輸出精度較高較清晰的地物圖像。 本文結(jié)合低通濾波原理，提出一種基于地物光譜矢量特征的濾波去噪方法MFS，用定標(biāo)后的Landsat-7ETM+地物反射率圖像廣義歸一光譜進(jìn)行濾波處理實(shí)驗(yàn)，保持圖像地物光譜特征、邊沿特征、紋理特征、地形因子、地物BRDF因子、混合像元每種地物占比因子前提下，消減了噪聲，提高了像元值精度及圖像清晰度。 MFS不需要DTM數(shù)據(jù)能適應(yīng)地形變化，山區(qū)圖像去噪與平原區(qū)等效、暗區(qū)圖像去噪與亮區(qū)等效、全景圖像去噪效果均衡一致。MFS維持原圖像物理量綱不變，去噪同時(shí)增強(qiáng)地物信號(hào)，，提高了圖像信噪比，適用于遙感圖像預(yù)處理。 全文共分為五章，第一章為緒論，主要介紹研究背景、國(guó)內(nèi)外研究現(xiàn)狀、遙感圖像去噪的經(jīng)典算法以及研究背景與意義。第二章首先介紹地物反射特征的描述方法，然后詳細(xì)論述歸一光譜矢量理論以及由歸一光譜矢量推出的廣義歸一光譜矢量理論，最后介紹了MFS濾波模型的算法。第三章主要介紹本文濾波處理實(shí)現(xiàn)的開(kāi)發(fā)語(yǔ)言C#以及根據(jù)該理論所進(jìn)行編程實(shí)現(xiàn)的處理程序。第四章主要是結(jié)合前面幾章的內(nèi)容，以Landsat-7ETM+遙感圖像為實(shí)驗(yàn)數(shù)據(jù)，對(duì)ETM+第1波段進(jìn)行去噪處理，與目前常用的4種濾波方法進(jìn)行比較，MFS去噪效果優(yōu)勢(shì)比較明顯，可望取代遙感圖像現(xiàn)行去噪方法，有一定應(yīng)用價(jià)值。第五章為本文的結(jié)論，主要是對(duì)本文所取得的進(jìn)步與存在的問(wèn)題以及對(duì)下一步工作的展望。
[Abstract]:The remote sensing images obtained by ground observation of ETM remote sensor have high frequency components of atmospheric interference, residual errors left by image processing in the early stage and other errors with unknown sources, so it is necessary to Denoise in practical applications. In order to improve the clarity of the image, scholars have explored many methods, among which the low-pass filtering effect is better, which is widely used by remote sensing image processing software, but the filtering effect is still not ideal, the interior of the ground object is weak and clear, and the edge of the ground object is weakly visible and gelatinized. The noise is weakened and the signal is weakened at the same time. The denoising method needs to be improved and the new method needs to be explored. It is hoped that the new method can weaken the noise, enhance the signal and output the ground object image with high accuracy and clarity. In this paper, based on the principle of low-pass filtering, a filtering denoising method based on the spectral vector characteristics of ground objects is proposed. MFS, carries on the filtering experiment with the generalized normalization spectrum of the calibrated Landsat-7ETM figure reflectivity image to maintain the spectral characteristics of the image ground objects. On the premise of edge feature, texture feature, terrain factor, ground object BRDF factor and mixed pixel ratio factor, the noise is reduced and the pixel value accuracy and image clarity are improved. MFS does not need DTM data to adapt to terrain changes, mountain image denoising is equivalent to plain area, dark area image denoising is equivalent to bright area, panoramic image denoising effect is the same. MFS keeps the physical dimension of the original image unchanged, denoising and enhancing ground object signal. The signal-to-noise ratio (SNR) of the image is improved and it is suitable for remote sensing image preprocessing. The full text is divided into five chapters. The first chapter is the introduction, which mainly introduces the research background, the research status at home and abroad, the classical algorithm of remote sensing image denoising, as well as the research background and significance. In the second chapter, the description method of reflection characteristics of ground objects is introduced, and then the theory of normalized spectral vector and the theory of generalized normalized spectral vector derived from normalized spectral vector are discussed in detail. Finally, the algorithm of MFS filtering model is introduced. The third chapter mainly introduces the development language C # and the programming program according to the theory. The fourth chapter mainly combines the contents of the previous chapters, takes Landsat-7ETM remote sensing image as the experimental data, carries on the denoising processing to the first band of ETM, compared with the four filtering methods commonly used at present, the MFS denoising effect is more obvious. It is expected to replace the current denoising method of remote sensing image and has certain application value. The fifth chapter is the conclusion of this paper, mainly on the progress and existing problems of this paper, as well as the prospect of the next work.
【學(xué)位授予單位】：東北師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP751

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 王相海;張洪為;李放;;遙感圖像高斯與椒鹽噪聲的PDE混合去噪模型研究[J];測(cè)繪學(xué)報(bào);2010年03期

2 張晶晶;方勇華;;基于Contourlet變換的遙感圖像去噪新算法[J];光學(xué)學(xué)報(bào);2008年03期

3 盧云君;李健軍;張偉;鄭小兵;;一種新型的紅外輻亮度絕對(duì)定標(biāo)方法研究[J];光學(xué)學(xué)報(bào);2010年05期

4 郭強(qiáng),陳桂林;基于二維GFVC卡爾曼濾波的遙感圖像信噪比分析[J];紅外技術(shù);2003年02期

5 聞建光;柳欽火;肖青;劉強(qiáng);李小文;;復(fù)雜山區(qū)光學(xué)遙感反射率計(jì)算模型[J];中國(guó)科學(xué)(D輯:地球科學(xué));2008年11期

6 陳春;劉成玉;張樹(shù)清;;基于地物光譜矢量空間的遙感圖像大氣影響校正[J];中國(guó)科學(xué):地球科學(xué);2012年06期

7 余岸竹;姜挺;唐志華;張笑微;于瑤瑤;;一種基于壓縮感知的遙感影像混合去噪模型[J];測(cè)繪科學(xué)技術(shù)學(xué)報(bào);2013年01期

8 梅元?jiǎng)?何玉慶;;低維特征空間中基于旋轉(zhuǎn)圖像的三維環(huán)境模型配準(zhǔn)方法[J];中國(guó)科學(xué):技術(shù)科學(xué);2014年01期

9 張九星;黑保琴;李盛陽(yáng);李緒志;;基于變分方法的遙感圖像去噪研究[J];遙感技術(shù)與應(yīng)用;2010年04期

10 趙高長(zhǎng);張磊;武風(fēng)波;;改進(jìn)的中值濾波算法在圖像去噪中的應(yīng)用[J];應(yīng)用光學(xué);2011年04期

本文編號(hào)：2479904