發(fā)布時間：2018-01-26 03:21

  本文關鍵詞： 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　出處：《哈爾濱工業(yè)大學》2014年碩士論文　論文類型：學位論文

【摘要】：本文從我國某型號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用的需求出發(fā)，設計、實現(xiàn)、應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現(xiàn)了大氣背景測量數(shù)據(jù)的應用，在數(shù)據(jù)質(zhì)量評價與篩選、圖像紋理特征描述、數(shù)據(jù)挖掘分類技術(shù)及機器學習方面開展了深入的研究工作，實現(xiàn)、應用了具有自學習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)總體設計研究方面，針對遙感數(shù)據(jù)定量化處理的實際需要，對天基大氣背景測量處理系統(tǒng)進行了總體設計，，按功能將系統(tǒng)進一步劃分為相互獨立的處理模塊，并對各模塊的實現(xiàn)功能、實現(xiàn)方法及基本處理模型進行了設計。 在面向數(shù)據(jù)挖掘的數(shù)據(jù)預處理方法研究方面，以紅外大氣背景輻射亮度圖像的誤差分析為切入點，對數(shù)據(jù)的質(zhì)量進行評價，對誤差計算結(jié)果進行分析，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行描述，采用灰度共生矩陣的方法對圖像的紋理特征進行提取描述，通過比較試驗確定了灰度共生矩陣中的各項參數(shù)，獲得了12個紋理特征參數(shù)，對紋理特征參數(shù)的描述結(jié)果進行分析，將其按描述能力初步劃分為三類。 在基于支持向量機的圖像分類方法研究方面，使用非線性可分支持向量機分類器按圖像中的云分布情況對數(shù)據(jù)進行分類，通過試驗進一步修正了對紋理特征參數(shù)的分類，確定了用于學習和分類的紋理描述參數(shù)，實現(xiàn)了較高的分類精度；設計了支持向量機分類器的自學習算法，試驗結(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，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。 本文的研究工作，可為我國大氣背景定量遙感測量后續(xù)型號的地面處理系統(tǒng)設計與建設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.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP311.13;TP751

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本文編號：1464521