發(fā)布時(shí)間：2018-03-23 21:27

  本文選題：遙感數(shù)據(jù)　切入點(diǎn)：影像修復(fù)　出處：《武漢大學(xué)》2016年博士論文

【摘要】：隨著衛(wèi)星和傳感器技術(shù)的不斷創(chuàng)新,越來越多的遙感衛(wèi)星觀測(cè)數(shù)據(jù)得以獲取。其中,遙感時(shí)間序列數(shù)據(jù)被廣泛應(yīng)用于區(qū)域以及全球環(huán)境變化研究中。隨著研究的深度開展,對(duì)遙感時(shí)間序列數(shù)據(jù)時(shí)空連續(xù)性和完整性的要求越來越高。但是由于在數(shù)據(jù)獲取的過程中,不可避免的會(huì)受到觀測(cè)條件和傳感器故障等因素的影響造成大量信息缺失,使得遙感時(shí)間序列數(shù)據(jù)呈現(xiàn)時(shí)間不連續(xù)、空間不完整狀態(tài),嚴(yán)重的阻礙了數(shù)據(jù)的進(jìn)一步應(yīng)用。如何在現(xiàn)有觀測(cè)條件下,利用已有的遙感時(shí)間序列數(shù)據(jù)重建出高質(zhì)量的時(shí)空連續(xù)的、完整的遙感時(shí)間序列數(shù)據(jù)促進(jìn)了時(shí)域?yàn)V波和時(shí)域重建技術(shù)的發(fā)展。本文以提高遙感時(shí)間序列數(shù)據(jù)的時(shí)空連續(xù)性和完整性為主線,針對(duì)遙感時(shí)間序列數(shù)據(jù)產(chǎn)品中存在的問題和當(dāng)下現(xiàn)有的時(shí)域?yàn)V波和時(shí)域重建方法的缺陷與不足,提出新的時(shí)域?yàn)V波和時(shí)域重建算法,以重建出高質(zhì)量的遙感時(shí)間序列數(shù)據(jù)。本文主要工作總結(jié)為以下幾方面：(1)以提高遙感時(shí)間序列數(shù)據(jù)的時(shí)間連續(xù)性和空間完整性為主線,分析了當(dāng)下遙感時(shí)間序列數(shù)據(jù)產(chǎn)品存在的問題和遙感影像修復(fù)方法的研究現(xiàn)狀,總結(jié)了它們?cè)趯?shí)際應(yīng)用中存在的缺陷與不足,提出本文的研究目標(biāo)。并且具體介紹了當(dāng)前的時(shí)域?yàn)V波和時(shí)域重建算法以及論文中用到的評(píng)價(jià)方法與指標(biāo)。(2)提出移動(dòng)加權(quán)諧波分析的NDVI時(shí)域?yàn)V波方法。為了改善諧波分析方法(HANTS)重建結(jié)果出現(xiàn)過度擬合或過度平滑的現(xiàn)象,本文在原方法的基礎(chǔ)上引入移動(dòng)支持域,對(duì)時(shí)間序列數(shù)據(jù)進(jìn)行移動(dòng)加權(quán)局部處理。在每一個(gè)移動(dòng)支持域中,通過三次樣條法為每個(gè)數(shù)據(jù)點(diǎn)分配權(quán)值。在每個(gè)移動(dòng)支持域中對(duì)數(shù)據(jù)進(jìn)行擬合,并且通過權(quán)值分配控制參考數(shù)據(jù)對(duì)重建數(shù)據(jù)的影響程度：同時(shí)由于在移動(dòng)支持域內(nèi)待擬合數(shù)據(jù)少,使得諧波個(gè)數(shù)更加容易確定。此外,本文針對(duì)NDVI時(shí)間序列數(shù)據(jù)的特點(diǎn)設(shè)計(jì)四步處理流程對(duì)其進(jìn)行處理,使得重建數(shù)據(jù)逼近原始NDVI的上包絡(luò)線,從而更精確的獲得植被的真實(shí)變化趨勢(shì)。實(shí)驗(yàn)證明,該方法不僅可以很好的識(shí)別噪聲點(diǎn)并且使得重建結(jié)果逼近NDVI時(shí)間序列的上包絡(luò)線；還能夠正確的估計(jì)植被休眠期的NDVI值,很好的處理NDVI時(shí)間序列中出現(xiàn)連續(xù)波動(dòng)的現(xiàn)象,在絕大多數(shù)情況下魯棒性強(qiáng)。(3)提出了諧波分析與泊松方程協(xié)同的地表反射率時(shí)域重建方法。針對(duì)已有方法無法有效的實(shí)現(xiàn)對(duì)每天數(shù)據(jù)的重建這一缺陷,本文提出的時(shí)域重建方法不僅可以重建每天的反射率時(shí)間序列數(shù)據(jù),而且在保留未缺失區(qū)域原始值的前提下只對(duì)缺失區(qū)域進(jìn)行填補(bǔ),實(shí)現(xiàn)了真正意義的時(shí)域重建。主要思想是首先通過多年數(shù)據(jù)間的聯(lián)合加權(quán)平均對(duì)待重建年份的缺失數(shù)據(jù)進(jìn)行初步填補(bǔ),為時(shí)間域重建提供足夠的初始值；基于這些初始值通過時(shí)域?yàn)V波算法對(duì)第一步未填補(bǔ)的區(qū)域進(jìn)行填補(bǔ),同時(shí)對(duì)已經(jīng)填補(bǔ)的區(qū)域進(jìn)行調(diào)整；最后通過泊松圖像編輯對(duì)修復(fù)區(qū)域的值進(jìn)行調(diào)整,實(shí)現(xiàn)重建數(shù)據(jù)的空間無縫。實(shí)驗(yàn)結(jié)果表明該方法可以重建出每天的時(shí)空連續(xù)的地表反射率產(chǎn)品,不僅能夠保持?jǐn)?shù)據(jù)時(shí)域的連續(xù)性,也能保證數(shù)據(jù)空間的完整性,同時(shí),重建的地表反射率數(shù)據(jù)也保持了光譜的完整性。(4)提出了顧及物理約束的地表溫度時(shí)域重建方法�？紤]到云層會(huì)對(duì)地表溫度產(chǎn)生影響,本論文提出在地表溫度數(shù)據(jù)的重建過程中充分考慮到云層對(duì)地表溫度的影響,耦合能表現(xiàn)反映影響程度的物理量,通過物理約束手段重建出更符合真實(shí)情況的地表溫度產(chǎn)品。該方法首先依據(jù)遙感時(shí)間序列產(chǎn)品時(shí)間上的依存性重建出晴空條件下高質(zhì)量產(chǎn)品,然后建立物理約束輔助數(shù)據(jù)與待重建數(shù)據(jù)之間的關(guān)系,通過對(duì)關(guān)系模型參數(shù)進(jìn)行高精度訓(xùn)練,將云覆蓋區(qū)域的信息進(jìn)行重建。實(shí)驗(yàn)結(jié)果表明該方法不僅可以提高晴空條件下質(zhì)量低的像元的質(zhì)量,而且可以對(duì)云覆蓋區(qū)域的像元進(jìn)行高精度的重建,最終重建出高質(zhì)量的每天的地表溫度數(shù)據(jù)。
[Abstract]:With the continuous innovation of satellite and sensor technology, remote sensing satellite data to get more and more. Among them, the remote sensing data of time series is widely used in regional and global environmental change research. With the depth of research carried out on the data of time series remote sensing, spatial continuity and integrity of the increasingly high demand. But in due process the data acquisition, will be influenced by the observation condition and sensor fault caused by a large number of factors such as lack of information, showing the time discontinuous remote sensing data of time series, space is not complete, seriously hinder the further application of data. How the existing observation condition, using the remote sensing data of time series reconstruction in time and space the high quality of the continuous, remote sensing time series data integrity and promote the development of time domain filtering and time domain reconstruction technology. In this paper. High spatial remote sensing data of time series continuity and integrity as the main line, aiming at the existing defects of remote sensing data products in time series and the existing time-domain filtering and time domain reconstruction method and the insufficiency, proposed time-domain filtering and time domain reconstruction algorithm of the remote sensing data of time series to reconstruct high quality. This paper the work summarized as follows: (1) to improve the remote sensing data of time series time continuity and spatial integrity as the main line, analyzes the research status of the existing remote sensing data of time series products and remote sensing image restoration method, summarizes their shortcomings in the practical application and the insufficiency, puts forward the research target in this paper. And introduces the specific evaluation methods and indicators used in the time domain and time domain filtering and reconstruction algorithm in this paper. (2) proposed moving weighted harmonic analysis ND VI time domain filtering method. In order to improve the harmonic analysis method (HANTS) reconstruction results over fitting or over smoothing phenomenon, this paper introduces the mobile support domain on the basis of the original method, the time series data of mobile weighted local processing. In each mobile support domain, through the three spline method for each data point distribution weights. In each mobile support for data fitting in the domain, and the distribution of weight control reference data impact on the reconstruction data. At the same time because in the mobile support domain to fit the data, the number of harmonic is more easily determined. In addition, the four step process to process it according to the characteristics of NDVI time sequence data design, making the reconstruction of data envelope approximation of the original NDVI, so as to obtain more accurate vegetation real trend. The experimental results show that this method not only can be very good On the envelope of noise and the reconstruction results approach NDVI time series; also can estimate the correct vegetation dormancy period NDVI, continuous wave phenomena appear NDVI time series well, in most cases robust. (3) proposed the surface reflectance time domain reconstruction method for harmonic coordination wave analysis and Poisson equation. Because the existing methods can not effectively realize the reconstruction of the defect data every day, the time domain reconstruction method proposed in this paper can not only reflectance time series data reconstruction every day, and keeping the original value under the missing regions not only to fill the missing regions, realize the true meaning of the time domain reconstruction the main idea is the lack of data. Firstly, combined with weighted data between the average years of reconstruction years were preliminarily treated filled, for the time domain reconstruction early enough Initial value; these initial values of the first step of filling the area filled by temporal filtering algorithm based on simultaneous adjustment on the fill area; finally, Poisson image editing to adjust the value of the repair area, realize the reconstruction of data space seamlessly. Experimental results show that this method can reconstruct the continuous time every day the surface reflectance products, not only can maintain the continuity of data in the time domain, but also to ensure the integrity of the data space, at the same time, the surface reflectance data reconstruction also maintain the integrity of the spectrum. (4) the surface temperature time domain reconstruction method and Gu physical constraints. Considering the clouds will affect the surface temperature. The paper proposes the reconstruction process of surface temperature data in full consideration of the effect of clouds on the surface temperature, the coupling can reflect the influence degree of physical quantity, through physical ca. Beam means to rebuild more in line with the surface temperature product in the real situation. The method based on remote sensing time series on the time dependence of the reconstruction of high quality products under the clear sky condition, and then the relationship between the establishment of physical constraints and auxiliary data to be reconstructed data, through high precision training parameters of the relationship model, will rebuild information the cloud coverage area. The experimental results show that this method can not only improve the quality of low quality of the pixels under the clear sky condition, but also can be used for high precision reconstruction of pixel cloud coverage, finally reconstruct the surface temperature data of high quality every day.

【學(xué)位授予單位】：武漢大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：P237
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本文編號(hào)：1655225