發(fā)布時(shí)間：2018-05-07 08:21

  本文選題：主動(dòng)SAR數(shù)據(jù) + 被動(dòng)光學(xué)數(shù)據(jù)　； 參考：《中國(guó)礦業(yè)大學(xué)》2013年博士論文

【摘要】：地表環(huán)境監(jiān)測(cè)需要從土地利用/覆蓋分類、地表溫度反演、土壤濕度反演、地表形變提取等方面進(jìn)行研究,實(shí)現(xiàn)定性、定量、幾何、物理一體化綜合分析的目的。現(xiàn)有的遙感數(shù)據(jù)源中光學(xué)遙感影像,具有豐富的光譜色調(diào)變化,目標(biāo)檢測(cè)和識(shí)別都相對(duì)簡(jiǎn)單,但受天氣狀況和觀測(cè)時(shí)刻影響較大。相比之下主動(dòng)微波遙感影像具有全天候、全天時(shí)、多極化、穿透性好,紋理結(jié)構(gòu)信息豐富等特點(diǎn),但受頻率、極化方式、目標(biāo)幾何信息、介電特性等影響,而且受到SAR成像系統(tǒng)固有的斑點(diǎn)噪聲影響,使圖像的解譯能力降低,導(dǎo)致單獨(dú)使用雷達(dá)圖像來(lái)進(jìn)行分類和信息提取變得非常困難。因此主被動(dòng)遙感數(shù)據(jù)各有應(yīng)用優(yōu)勢(shì),為挖掘主被動(dòng)遙感信息在環(huán)境監(jiān)測(cè)方面的優(yōu)勢(shì),滿足地表環(huán)境分類、定性、定量、幾何、物理一體化監(jiān)測(cè)需求,本論文深入研究了基于主被動(dòng)數(shù)據(jù)協(xié)同處理的地表環(huán)境監(jiān)測(cè)體系結(jié)構(gòu)與技術(shù)方法,從信息融合與分類、協(xié)同目標(biāo)識(shí)別、協(xié)同參數(shù)反演與關(guān)聯(lián)分析四個(gè)層次開(kāi)展了研究,以具有代表性的城區(qū)、礦區(qū)、濕地等為應(yīng)用領(lǐng)域,綜合信息融合、特征提取與目標(biāo)識(shí)別、分類器集成、地表參數(shù)反演、地表形變監(jiān)測(cè)等多種技術(shù)手段,多方面、多角度研究了主被動(dòng)多源遙感信息協(xié)同處理在環(huán)境監(jiān)測(cè)方面的應(yīng)用。論文主要內(nèi)容和成果如下： (1)構(gòu)建了多源信息融合和多分類器集成的地表環(huán)境主被動(dòng)遙感數(shù)據(jù)協(xié)同分類方法。結(jié)果表明,改進(jìn)的小波信息融合和Bagging完全樣本集的SMO分類器集成方法具有穩(wěn)健的提高分類精度的能力。通過(guò)IHS算法對(duì)小波融合方法進(jìn)行改進(jìn),通過(guò)樣本篩選策略對(duì)分類器集成方法進(jìn)行優(yōu)選,將數(shù)據(jù)信息融合和分類器技術(shù)集成應(yīng)用于土地利用覆蓋分類。相比其他融合方法,改進(jìn)的融合方法獲取最優(yōu)融合結(jié)果；融合后分類結(jié)果驗(yàn)證了基于多分類器集成方法能夠在一定程度上提高分類精度。 (2)提出了基于主被動(dòng)遙感數(shù)據(jù)多特征組合的多分類器集成分類策略。通過(guò)等值權(quán)重對(duì)主被動(dòng)遙感數(shù)據(jù)的光譜特征、紋理特征、極化特征進(jìn)行組合,通過(guò)并聯(lián)、串聯(lián)策略對(duì)多種分類器進(jìn)行集成。結(jié)果表明光譜特征和SAR強(qiáng)度特征組合在使用串聯(lián)的分類器集成策略時(shí)獲取最高分類精度,光譜特征和SAR極化特征組合在使用并聯(lián)的分類器集成策略時(shí)獲取最優(yōu)分類精度。提出的基于主被動(dòng)遙感數(shù)據(jù)多特征組合的多分類器集成分類方法對(duì)不同地物類型的提取精度均有不同程度的提高,對(duì)難以區(qū)分的復(fù)雜研究區(qū)域改進(jìn)明顯,極化、光譜、紋理特征集成策略適用于多分類器串聯(lián)集成策略,極化特征和光譜特征集成策略在多分類器并聯(lián)協(xié)同中獲得最高分類精度。 (3)改進(jìn)了基于典型地類目標(biāo)識(shí)別基礎(chǔ)的決策分類方法。利用特征因子、紋理特征、投票決策改進(jìn)基于空間關(guān)聯(lián)度指數(shù)的人類居住地識(shí)別算法,實(shí)現(xiàn)基于協(xié)同主被動(dòng)遙感數(shù)據(jù)多源特征的目標(biāo)識(shí)別與分類。實(shí)驗(yàn)結(jié)果表明協(xié)同主被動(dòng)遙感數(shù)據(jù)多特征目標(biāo)識(shí)別基礎(chǔ)上的決策分類方法不僅能夠提高單一地物類別識(shí)別精度,對(duì)整體分類精度也有明顯提高。 (4)設(shè)計(jì)了基于主被動(dòng)遙感數(shù)據(jù)的地表環(huán)境幾何、物理、定性、定量一體化監(jiān)測(cè)方法。利用熱紅外數(shù)據(jù)反演地表溫度參數(shù),利用主動(dòng)SAR數(shù)據(jù)和光學(xué)數(shù)據(jù)協(xié)同反演土壤水分,利用兩軌差分方法提取地表形變信息,實(shí)現(xiàn)地表覆蓋、地表溫度、淺層土壤水分和地下變形的‘地空一體化’協(xié)同分析的基本條件。通過(guò)關(guān)聯(lián)分析,初步得出地表覆蓋類型與地表溫度、地表形變、土壤濕度的關(guān)系；高溫?zé)釄?chǎng)與地表形變、地表覆蓋類型的關(guān)系。最后結(jié)合實(shí)例,應(yīng)用CA_Markov模型、RUSLE模型對(duì)土地利用／覆蓋類型、地表溫度覆蓋等級(jí)的變化趨勢(shì)進(jìn)行模擬與分析。結(jié)果表明協(xié)同主被動(dòng)多源數(shù)據(jù)研究地表覆蓋狀態(tài)和地表參數(shù)關(guān)系,是充分利用主被動(dòng)數(shù)據(jù)協(xié)同優(yōu)勢(shì),實(shí)現(xiàn)‘地、空一體化監(jiān)測(cè)’和快速地表環(huán)境集成監(jiān)測(cè)的有效方法,體現(xiàn)了主被動(dòng)遙感信息協(xié)同處理地表環(huán)境監(jiān)測(cè)體系在實(shí)際應(yīng)用處理中的優(yōu)勢(shì)。
[Abstract]:Surface environmental monitoring needs to be studied in aspects of land use / cover classification, surface temperature inversion, soil moisture inversion, surface deformation extraction and so on. The purpose of integrated analysis of qualitative, quantitative, geometric and physical integration can be achieved. The existing optical remote sensing images in the remote sensing data source have rich spectral tone changes, target detection and recognition. Relatively simple, but influenced by weather condition and observation time, the active microwave remote sensing image has the characteristics of all-weather, multi polarization, good penetration, and rich texture information, but influenced by frequency, polarization, target geometry and dielectric properties, and is influenced by speckle noise inherent in SAR imaging system. As a result, the ability of image interpretation can be reduced, and it is very difficult to use radar images to classify and extract information alone. Therefore, the main and passive remote sensing data have their advantages to mine the advantages of the passive remote sensing information in environmental monitoring and meet the integrated monitoring requirements of the surface environment classification, qualitative, quantitative, geometric, and physical. In this paper, the structure and technology of surface environment monitoring system based on CO processing of active and passive data are studied. The research is carried out from four levels, including information fusion and classification, cooperative target recognition, collaborative parameter inversion and association analysis, which are representative area, mining area, wet land and so on as application fields, integrated information fusion, feature extraction and extraction. Target recognition, classifier integration, surface parameter inversion, surface deformation monitoring and so on, many aspects and multi angles are used to study the application of cooperative processing of passive multi-source remote sensing information in environmental monitoring. The main contents and achievements of this paper are as follows:
(1) construction of multi source information fusion and multi classifier integration of surface environment and passive remote sensing data synergetic classification method. The results show that the improved wavelet information fusion and the Bagging complete sample set SMO classifier integration method has a robust ability to improve the classification accuracy. Through the IHS algorithm to improve the wavelet fusion method, through the sample This filtering strategy optimizes the classifier ensemble method, and applies the data information fusion and classifier technology to the land use coverage classification. Compared with other fusion methods, the improved fusion method is used to obtain the optimal fusion results, and the fusion results verify that the multi classifier integration method can improve the score to a certain extent. Class precision.
(2) a multi classifier ensemble classification strategy based on the multi feature combination of the passive remote sensing data is proposed, which combines the spectral features, the texture features and the polarization characteristics of the passive remote sensing data by the equivalent weight, and integrates various classifiers through parallel and series strategy. The results show that the spectral features and the SAR intensity features are combined in use. In series classifier ensemble strategies, the highest classification accuracy is obtained. Spectral features and SAR polarization features are combined to obtain the optimal classification accuracy when using a parallel classifier ensemble strategy. The proposed multi classifier ensemble classification method based on the multi feature combination of the passive remote sensing data has different degrees of extraction accuracy for different terrain types. The integration strategy of polarization, spectrum and texture feature is suitable for the multi classifier series integration strategy. The polarization feature and spectral feature integration strategy can obtain the highest classification precision in the parallel collaboration of multiple classifiers.
(3) the decision classification method based on typical target recognition base is improved. Using feature factor, texture feature and voting decision, the human residence recognition algorithm based on spatial correlation index is improved to realize target recognition and classification based on multi source features of cooperative passive remote sensing data. Experimental results show cooperative passive remote sensing data. The decision classification method based on multi feature target recognition can not only improve the accuracy of single object classification, but also improve the overall classification accuracy.
(4) the surface environment geometry, physical, qualitative and quantitative integrated monitoring methods based on the passive remote sensing data are designed. Using the thermal infrared data to invert the surface temperature parameters, use the active SAR data and the optical data to coordinate the soil moisture, and use the two track differential method to extract the surface deformation information, and realize the surface cover, surface temperature and shallow layer. The basic conditions for the synergistic analysis of soil water and underground deformation are "ground space integration". Through correlation analysis, the relationship between surface cover type and surface temperature, surface deformation and soil moisture; the relationship between high temperature field and surface deformation and surface cover type. Finally, the application of CA_Markov model and RUSLE model to land The results show that the relationship between the surface coverage and the surface parameters of the cooperative principal and passive multi source data is an effective method to make full use of the synergistic advantages of the active and passive data to realize the integrated monitoring of ground, air integration and rapid surface environment. The advantages of active passive remote sensing information in collaborative processing of surface environmental monitoring system in practical application are presented.

【學(xué)位授予單位】：中國(guó)礦業(yè)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2013
【分類號(hào)】：P237

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