本文關(guān)鍵詞： 定量遙感 定量化處理 遙感圖像數(shù)據(jù)挖掘 灰度共生矩陣 支持向量機(jī)　出處：《哈爾濱工業(yè)大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：本文從我國某型號(hào)紅外大氣背景遙感衛(wèi)星對數(shù)據(jù)定量化處理及后期應(yīng)用的需求出發(fā)，設(shè)計(jì)、實(shí)現(xiàn)、應(yīng)用了天基大氣背景測量數(shù)據(jù)定量化處理系統(tǒng)；采用數(shù)據(jù)挖掘的思想與方法實(shí)現(xiàn)了大氣背景測量數(shù)據(jù)的應(yīng)用，在數(shù)據(jù)質(zhì)量評價(jià)與篩選、圖像紋理特征描述、數(shù)據(jù)挖掘分類技術(shù)及機(jī)器學(xué)習(xí)方面開展了深入的研究工作，實(shí)現(xiàn)、應(yīng)用了具有自學(xué)習(xí)能力的大氣背景測量圖像分類系統(tǒng)。本文的主要工作及成果如下： 在天基大氣背景測量處理系統(tǒng)總體設(shè)計(jì)研究方面，針對遙感數(shù)據(jù)定量化處理的實(shí)際需要，對天基大氣背景測量處理系統(tǒng)進(jìn)行了總體設(shè)計(jì)，，按功能將系統(tǒng)進(jìn)一步劃分為相互獨(dú)立的處理模塊，并對各模塊的實(shí)現(xiàn)功能、實(shí)現(xiàn)方法及基本處理模型進(jìn)行了設(shè)計(jì)。 在面向數(shù)據(jù)挖掘的數(shù)據(jù)預(yù)處理方法研究方面，以紅外大氣背景輻射亮度圖像的誤差分析為切入點(diǎn)，對數(shù)據(jù)的質(zhì)量進(jìn)行評價(jià)，對誤差計(jì)算結(jié)果進(jìn)行分析，研究了各誤差分量對數(shù)據(jù)質(zhì)量的影響，最后綜合各種因素確定數(shù)據(jù)的篩選原則；選用圖像的紋理特征對圖像中云的分布情況進(jìn)行描述，采用灰度共生矩陣的方法對圖像的紋理特征進(jìn)行提取描述，通過比較試驗(yàn)確定了灰度共生矩陣中的各項(xiàng)參數(shù)，獲得了12個(gè)紋理特征參數(shù)，對紋理特征參數(shù)的描述結(jié)果進(jìn)行分析，將其按描述能力初步劃分為三類。 在基于支持向量機(jī)的圖像分類方法研究方面，使用非線性可分支持向量機(jī)分類器按圖像中的云分布情況對數(shù)據(jù)進(jìn)行分類，通過試驗(yàn)進(jìn)一步修正了對紋理特征參數(shù)的分類，確定了用于學(xué)習(xí)和分類的紋理描述參數(shù)，實(shí)現(xiàn)了較高的分類精度；設(shè)計(jì)了支持向量機(jī)分類器的自學(xué)習(xí)算法，試驗(yàn)結(jié)果表明其分類能力優(yōu)于傳統(tǒng)分類器，克服了無效參數(shù)造成分類性能下降的問題。 在定量化處理系統(tǒng)及圖像分類系統(tǒng)的實(shí)現(xiàn)研究方面，完成了天基紅外大氣背景測量處理系統(tǒng)的建設(shè)，系統(tǒng)已投入使用，性能穩(wěn)定，定量處理精度高；研發(fā)的輻射亮度圖像分類系統(tǒng)軟件，可完成數(shù)據(jù)篩選、紋理描述及圖像分類的數(shù)據(jù)挖掘分類全過程處理任務(wù)。 本文的研究工作，可為我國大氣背景定量遙感測量后續(xù)型號(hào)的地面處理系統(tǒng)設(shè)計(jì)與建設(shè)提供理論方法與技術(shù)支持。
[Abstract]:This paper designs and implements a space-based atmospheric background measurement data quantification processing system based on the requirement of a certain type of infrared atmospheric background remote sensing satellite and its later application. The idea and method of data mining are used to realize the application of atmospheric background measurement data, in data quality evaluation and screening, image texture feature description. The classification technology of data mining and machine learning have been deeply studied and realized, and the image classification system of atmospheric background measurement with self-learning ability has been implemented. The main work and results of this paper are as follows: In the aspect of the overall design and research of space-based atmospheric background measurement and processing system, the overall design of space-based atmospheric background measurement and processing system is carried out according to the actual needs of quantitative processing of remote sensing data. According to the function, the system is further divided into independent processing modules, and the realization function, realization method and basic processing model of each module are designed. In the research of data preprocessing method for data mining, the error analysis of infrared atmospheric background radiance image is taken as the starting point, the quality of data is evaluated, and the error calculation results are analyzed. The influence of each error component on data quality is studied. Finally, the screening principle of data is determined by synthesizing all kinds of factors. The distribution of cloud in the image is described by the texture feature of the image, and the texture feature of the image is extracted and described by the method of gray level co-occurrence matrix. The parameters in the gray level co-occurrence matrix are determined by comparison experiments, and 12 texture feature parameters are obtained. The description results of the texture feature parameters are analyzed and divided into three categories according to the description ability. In the research of image classification based on support vector machine, the nonlinear separable support vector machine classifier is used to classify the data according to the cloud distribution in the image. Through experiments, the classification of texture feature parameters is further corrected, and the texture description parameters for learning and classification are determined, and the classification accuracy is achieved. The self-learning algorithm of SVM classifier is designed. The experimental results show that the classification ability of SVM classifier is better than that of traditional classifier. In the realization of quantitative processing system and image classification system, the construction of space-based infrared atmospheric background measurement and processing system has been completed, the system has been put into use, the performance of the system is stable, and the quantitative processing accuracy is high. The software of radiance image classification system can complete the whole process of data filtering, texture description and image classification. The research work in this paper can provide theoretical method and technical support for the design and construction of ground processing system for quantitative remote sensing measurement of atmospheric background in China.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP311.13;TP751

【參考文獻(xiàn)】

相關(guān)期刊論文 前7條

1 丁世飛;齊丙娟;譚紅艷;;支持向量機(jī)理論與算法研究綜述[J];電子科技大學(xué)學(xué)報(bào);2011年01期

2 黃家潔,萬幼川,劉良明;MODIS的特性及其應(yīng)用[J];地理空間信息;2003年04期

3 青巖;“空間中段監(jiān)視”試驗(yàn)衛(wèi)星概貌[J];國際太空;2003年07期

4 盧春蓮,趙雪,周彥平;污染對空間光學(xué)系統(tǒng)的影響分析[J];哈爾濱工業(yè)大學(xué)學(xué)報(bào);2005年02期

5 李琳;張曉龍;;基于RBF核的SVM學(xué)習(xí)算法的優(yōu)化計(jì)算[J];計(jì)算機(jī)工程與應(yīng)用;2006年29期

6 王苗;柴瑞敏;;一種改進(jìn)的決策樹分類屬性選擇方法[J];計(jì)算機(jī)工程與應(yīng)用;2010年08期

7 于杰;肖國強(qiáng);代毅;;粘連蠶卵計(jì)數(shù)方法的研究[J];計(jì)算機(jī)工程與應(yīng)用;2011年08期

本文編號(hào)：1464521