基于小波核滤波器和稀疏表示的遥感图像融合
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摘要
随着遥感技术的发展，其应用也越来越广泛，在地学科学、农业、气象、林业、城市规划、环境监测等等领域均有不同程度的应用。然而，由于遥感传感器技术本身的限制，所获得的遥感数据往往不能反映出区域的全部信息。为了更好的理解该地域的内容，将不同遥感器获得的图像信息进行融合便成了一项十分经济且有效的方案。近年来，为了提高对遥感图像的解译能力，信息融合的技术被引入到融合多遥感器图像及遥感卫星图像中。
本论文以遥感图像融合为研究背景，结合国家自然科学基金、国家“863”计划、“973”计划以及“111”创新引智计划等项目的任务与需求，利用多尺度几何分析、机器学习方法和优化算法等工具，完成了遥感图像融合方法的研究工作。论文主要工作概括如下：
1.借助支撑矢量机逼近原理对图像进行逼近建模，由此实现以核函数来描述图像，并提出一种多尺度变换工具——小波核滤波器。将小波核滤波器应用在桥梁分类、移动和静止目标获取和识别数据库（Moving and Stationary TargetAcquisition and Recognition，MSTAR）的数据识别及合成孔径雷达图像去斑中。桥梁分类和MSTAR数据识别应用结果表明，图像数据经过小波核滤波器之后得到系数能更好的表达原图像的信息，而图像去斑的应用也显示出由于该滤波器具有平移不变性，针对去斑中出现的振铃效应基本清除。
2.将提出的小波核滤波器应用到遥感图像融合中。小波核滤波器具有多尺度性、平移不变性、完全重构性等，使得该滤波器在图像融合的应用中具有优势。针对多传感器图像的特点，以区域能量最大值作为融合策略，实现基于小波核滤波器的多传感器图像融合，并与其他多尺度变换工具如小波变换、非下采样的小波变换、Contourlet变换、非下采样的Contourlet变换进行比较。四组针对曼彻斯特大学图像融合库的多源图像融合结果表明，小波核滤波器应用于多传感器图像融合是有效的，克服了图像融合中常出现的振铃效应，细节保持较好，取得更为清晰的融合结果。针对多光谱与全色图像的融合问题，在小波核滤波器的基础上，提出两种融合策略：其一是与传统的亮度-色调-饱和度变换相结合，对亮度I分量进行处理，将全色图像的细节加入到I分量中；其二是采用改进的空间分辨率增加框架法（Amélioration de laRésolution Spatiale par Injection de Structures，ARSIS）作为融合框架，利用多尺度分析手段为多光谱图像补充上缺失的细节成分。随后给出的来自于光学卫星的多光谱图像结果表明，小波核滤波器能够应用在多光谱图像与全色图像的融合中，获取融合结果，两种融合框架均能获得所需的具有高分辨率的多光谱图像，为后续多光谱图像的处理及应用奠定了基础。
3.针对遥感图像融合问题，提出了小波核滤波器结合优化算法的遥感图像融合方法。首先，结合小波核滤波器，将粒子群算法应用到多传感器图像融合中。针对细节子带仍采用区域能量最大值的融合策略，而近似子带则选择粒子群算法去搜索得到一个最优的近似子带。实验结果表明，结合小波核滤波器和粒子群算法的方法是有效的，可以得到相对最优的融合结果。针对多光谱与全色图像的融合问题，结合小波核滤波器和克隆选择算法给出两种融合策略：其一是通过小波核滤波器中参数的变化，给出多组小波核滤波器，结合亮度-色调-饱和度变换获得多组融合结果，克隆选择算法用来寻找到最优权值组合给出最优融合结果；其二是利用克隆选择算法寻找最优的亮度I分量，得到一个最逼近全色图像的I分量进行随后的融合处理。结果表明，结合优化算法的融合策略能够找到最优值，得到相对最优的融合结果。
4.随着稀疏表示理论的发展，该理论已被成功的应用于图像处理领域中。由于图像能够采取稀疏表示的方式来得到系数，稀疏的系数用来表达源图包含的信息，因此利用该系数便可完成图像融合的要求。根据多源图像的特点及稀疏表示获得的稀疏系数的特点，给出五种融合策略下得到的融合结果，并进行了比较，选择出适合于稀疏系数的融合规则，并与传统的多尺度变换方法进行比较，结果表明稀疏表示理论应用到图像融合领域亦能获得较优的结果。针对多光谱图像融合的问题，首先参照多光谱图像的特点，将基于稀疏表示的超分辨方法应用到多光谱图像与全色图像的融合中，通过超分辨方法先获得对应于低分辨率多光谱图像的高分辨率图像，结合ARSIS框架与全色图像融合，获得了较好的结果。
5.考虑到多光谱图像融合的目的在于增加多光谱图像的细节信息含量，将二维经验模式分解引入到前文提到的基于广义的亮度色度饱和度变换和小波核滤波器结合的多光谱图像融合方法中，并且为了找到既能提高光谱性又增加细节信息的结果，将折中参数引入了融合方法中，由此获取高分辨率多光谱图像，使得新获取的图像能够在保持光谱特性的基础上增加尽可能多的细节信息。
With the development of remote sensing technology, its applications areincreasingly being used in varying degrees in geology science, agriculture, meteorology,forestry, urban planning, environmental monitoring fields, et al. However, due to thelimitations imposed by remote sensors, remote sensing data obtained often do not reflectall of the information from the geographical area. To better understand the content of thearea, the image information is merged by the different remote sensor data. It has becomea very economical and effective solution. In recent years, information fusion technologyhas been introduced into the process of multi-sensor and satellite image fusion in orderto improve the interpretation ability of remote sensing images.
In this dissertation, remote sensing image fusion is as the research background. Thefusion methods are proposed based on the multi-scale geometric analysis, machinelearning, evolutionary algorithm and other tools. These methods can achieve the taskand requirement of projects such as the National Natural Science Foundation, theNational ‘863’,‘973’ program and the The Fund for Foreign Scholars in UniversityResearch and Teaching Programs (the111Project), et al. The main work of thisdissertation is summarized as follows:
1. Images are approximated by using the principle of support vector machine anddescribed as kernel functions, moreover, a new multi-scale transform tool namedwavelet kernel filter (WKF) is proposed. WKF is applied in bridge classification,MSTAR recognition and SAR image de-speckling. Results of bridge classificationand MSTAR recognition validate that the WKF coefficients of images contain themore image information, and the application of speckle images also show thetranslational invariance due to the transformation, for speckle ringing effectsappear almost clear.
2. WKF is applied in remote sensing image fusion. It has multi-scale, translationinvariance and perfect reconstruction property. All the properties ensure that theWKF has advantages in image fusion. Considering the characteristics ofmulti-sensor image, the region energy maximum value is applied as fusion rule,where WKF is used to extract the fusion features. Fusion effect is carried out insome multi-scale geometric analysis tool wavelet, such as non-sampled wavelet,contourlet, non-sampled contourlet. Four group fusion results on the multi-sourceimage fusion library from University of Manchester validate that application of WKF in image fusion is effective to obtain better fused results. The fused resultscan avoid ringing effect and retain the detail information. For the fusion problemof multi-spectral and panchromatic image, two fusion strategies are proposed, thefirst one is combined with traditional Intensity-Hue-Saturation transform, whichprocesses intensity component; the other one adopts ARSIS (Amélioration de laRésolution Spatiale par Injection de Structures) concept for enriching the detailinformation in missing multi-spectral image. Fusion results demonstrate showsthat both fusion strategies can obtain the multiple spectral images with highresolution. It is helpful for the subsequent operation.
3. A new method is proposed by combining WKF and Particle Swarm Optimization(PSO) algorithm for remote sensing image fusion. The detail subbands andapproximation subband are extracted using WKF. The detail subbands from thedifferent images are fused according to the region energy maximum fusion rulesand the approximation ones are fused using the PSO. The fused image can beobtained by the inverse transformation of WKF. Experimental results validate thismethod is effective and achieves the better optimal fusion result. For the MS andPAN image fused problem, two strategies are given. The first one constructsmulti-WKFs through the change of parameters in WKF, ARSIS concept withclone selection algorithm (CSA) can find the optimal weighted values and thenobtain optimal fusion result. The second one attempts to find the optimal intensitycomponent by CSA. The fused results indicate both combing algorithms areeffective and can obtain better results.
4. In recent years, with the development of sparse representation theory, the imageprocess methods appear more and more applications by sparse representation.Considering the coefficients of images will be obtained by sparse representation,and finish fusion by these coefficients, and then we apply sparse representation inimage fusion. According to features of multi-sensor images and the coefficientsobtained by sparse representation, obtain fusion results by five fusion rules andgive the comparison results. According to characteristic of MS image, superresolution method by sparse representation method is applied in fusing MS andPAN image, and obtain the better results.
5. Then, a novel pan-sharpening method, GIHS-WKF-BEMD-TP, based on GIHStransformation and combined WKF with bi-dimensional empirical modedecomposition (BEMD), is applied in fusing MS and PAN images. The BEMD is a highly adaptive method; each intrinsic mode functions (IMF) from thedecomposition describes local salient information and produces better spatialresponse. Finally, a tradeoff parameter is used to control the fused image withboth high spectral and spatial performance in terms of quality indices and visualeffect effectively.
本论文以遥感图像融合为研究背景，结合国家自然科学基金、国家“863”计划、“973”计划以及“111”创新引智计划等项目的任务与需求，利用多尺度几何分析、机器学习方法和优化算法等工具，完成了遥感图像融合方法的研究工作。论文主要工作概括如下：
1.借助支撑矢量机逼近原理对图像进行逼近建模，由此实现以核函数来描述图像，并提出一种多尺度变换工具——小波核滤波器。将小波核滤波器应用在桥梁分类、移动和静止目标获取和识别数据库（Moving and Stationary TargetAcquisition and Recognition，MSTAR）的数据识别及合成孔径雷达图像去斑中。桥梁分类和MSTAR数据识别应用结果表明，图像数据经过小波核滤波器之后得到系数能更好的表达原图像的信息，而图像去斑的应用也显示出由于该滤波器具有平移不变性，针对去斑中出现的振铃效应基本清除。
2.将提出的小波核滤波器应用到遥感图像融合中。小波核滤波器具有多尺度性、平移不变性、完全重构性等，使得该滤波器在图像融合的应用中具有优势。针对多传感器图像的特点，以区域能量最大值作为融合策略，实现基于小波核滤波器的多传感器图像融合，并与其他多尺度变换工具如小波变换、非下采样的小波变换、Contourlet变换、非下采样的Contourlet变换进行比较。四组针对曼彻斯特大学图像融合库的多源图像融合结果表明，小波核滤波器应用于多传感器图像融合是有效的，克服了图像融合中常出现的振铃效应，细节保持较好，取得更为清晰的融合结果。针对多光谱与全色图像的融合问题，在小波核滤波器的基础上，提出两种融合策略：其一是与传统的亮度-色调-饱和度变换相结合，对亮度I分量进行处理，将全色图像的细节加入到I分量中；其二是采用改进的空间分辨率增加框架法（Amélioration de laRésolution Spatiale par Injection de Structures，ARSIS）作为融合框架，利用多尺度分析手段为多光谱图像补充上缺失的细节成分。随后给出的来自于光学卫星的多光谱图像结果表明，小波核滤波器能够应用在多光谱图像与全色图像的融合中，获取融合结果，两种融合框架均能获得所需的具有高分辨率的多光谱图像，为后续多光谱图像的处理及应用奠定了基础。
3.针对遥感图像融合问题，提出了小波核滤波器结合优化算法的遥感图像融合方法。首先，结合小波核滤波器，将粒子群算法应用到多传感器图像融合中。针对细节子带仍采用区域能量最大值的融合策略，而近似子带则选择粒子群算法去搜索得到一个最优的近似子带。实验结果表明，结合小波核滤波器和粒子群算法的方法是有效的，可以得到相对最优的融合结果。针对多光谱与全色图像的融合问题，结合小波核滤波器和克隆选择算法给出两种融合策略：其一是通过小波核滤波器中参数的变化，给出多组小波核滤波器，结合亮度-色调-饱和度变换获得多组融合结果，克隆选择算法用来寻找到最优权值组合给出最优融合结果；其二是利用克隆选择算法寻找最优的亮度I分量，得到一个最逼近全色图像的I分量进行随后的融合处理。结果表明，结合优化算法的融合策略能够找到最优值，得到相对最优的融合结果。
4.随着稀疏表示理论的发展，该理论已被成功的应用于图像处理领域中。由于图像能够采取稀疏表示的方式来得到系数，稀疏的系数用来表达源图包含的信息，因此利用该系数便可完成图像融合的要求。根据多源图像的特点及稀疏表示获得的稀疏系数的特点，给出五种融合策略下得到的融合结果，并进行了比较，选择出适合于稀疏系数的融合规则，并与传统的多尺度变换方法进行比较，结果表明稀疏表示理论应用到图像融合领域亦能获得较优的结果。针对多光谱图像融合的问题，首先参照多光谱图像的特点，将基于稀疏表示的超分辨方法应用到多光谱图像与全色图像的融合中，通过超分辨方法先获得对应于低分辨率多光谱图像的高分辨率图像，结合ARSIS框架与全色图像融合，获得了较好的结果。
5.考虑到多光谱图像融合的目的在于增加多光谱图像的细节信息含量，将二维经验模式分解引入到前文提到的基于广义的亮度色度饱和度变换和小波核滤波器结合的多光谱图像融合方法中，并且为了找到既能提高光谱性又增加细节信息的结果，将折中参数引入了融合方法中，由此获取高分辨率多光谱图像，使得新获取的图像能够在保持光谱特性的基础上增加尽可能多的细节信息。
With the development of remote sensing technology, its applications areincreasingly being used in varying degrees in geology science, agriculture, meteorology,forestry, urban planning, environmental monitoring fields, et al. However, due to thelimitations imposed by remote sensors, remote sensing data obtained often do not reflectall of the information from the geographical area. To better understand the content of thearea, the image information is merged by the different remote sensor data. It has becomea very economical and effective solution. In recent years, information fusion technologyhas been introduced into the process of multi-sensor and satellite image fusion in orderto improve the interpretation ability of remote sensing images.
In this dissertation, remote sensing image fusion is as the research background. Thefusion methods are proposed based on the multi-scale geometric analysis, machinelearning, evolutionary algorithm and other tools. These methods can achieve the taskand requirement of projects such as the National Natural Science Foundation, theNational ‘863’,‘973’ program and the The Fund for Foreign Scholars in UniversityResearch and Teaching Programs (the111Project), et al. The main work of thisdissertation is summarized as follows:
1. Images are approximated by using the principle of support vector machine anddescribed as kernel functions, moreover, a new multi-scale transform tool namedwavelet kernel filter (WKF) is proposed. WKF is applied in bridge classification,MSTAR recognition and SAR image de-speckling. Results of bridge classificationand MSTAR recognition validate that the WKF coefficients of images contain themore image information, and the application of speckle images also show thetranslational invariance due to the transformation, for speckle ringing effectsappear almost clear.
2. WKF is applied in remote sensing image fusion. It has multi-scale, translationinvariance and perfect reconstruction property. All the properties ensure that theWKF has advantages in image fusion. Considering the characteristics ofmulti-sensor image, the region energy maximum value is applied as fusion rule,where WKF is used to extract the fusion features. Fusion effect is carried out insome multi-scale geometric analysis tool wavelet, such as non-sampled wavelet,contourlet, non-sampled contourlet. Four group fusion results on the multi-sourceimage fusion library from University of Manchester validate that application of WKF in image fusion is effective to obtain better fused results. The fused resultscan avoid ringing effect and retain the detail information. For the fusion problemof multi-spectral and panchromatic image, two fusion strategies are proposed, thefirst one is combined with traditional Intensity-Hue-Saturation transform, whichprocesses intensity component; the other one adopts ARSIS (Amélioration de laRésolution Spatiale par Injection de Structures) concept for enriching the detailinformation in missing multi-spectral image. Fusion results demonstrate showsthat both fusion strategies can obtain the multiple spectral images with highresolution. It is helpful for the subsequent operation.
3. A new method is proposed by combining WKF and Particle Swarm Optimization(PSO) algorithm for remote sensing image fusion. The detail subbands andapproximation subband are extracted using WKF. The detail subbands from thedifferent images are fused according to the region energy maximum fusion rulesand the approximation ones are fused using the PSO. The fused image can beobtained by the inverse transformation of WKF. Experimental results validate thismethod is effective and achieves the better optimal fusion result. For the MS andPAN image fused problem, two strategies are given. The first one constructsmulti-WKFs through the change of parameters in WKF, ARSIS concept withclone selection algorithm (CSA) can find the optimal weighted values and thenobtain optimal fusion result. The second one attempts to find the optimal intensitycomponent by CSA. The fused results indicate both combing algorithms areeffective and can obtain better results.
4. In recent years, with the development of sparse representation theory, the imageprocess methods appear more and more applications by sparse representation.Considering the coefficients of images will be obtained by sparse representation,and finish fusion by these coefficients, and then we apply sparse representation inimage fusion. According to features of multi-sensor images and the coefficientsobtained by sparse representation, obtain fusion results by five fusion rules andgive the comparison results. According to characteristic of MS image, superresolution method by sparse representation method is applied in fusing MS andPAN image, and obtain the better results.
5. Then, a novel pan-sharpening method, GIHS-WKF-BEMD-TP, based on GIHStransformation and combined WKF with bi-dimensional empirical modedecomposition (BEMD), is applied in fusing MS and PAN images. The BEMD is a highly adaptive method; each intrinsic mode functions (IMF) from thedecomposition describes local salient information and produces better spatialresponse. Finally, a tradeoff parameter is used to control the fused image withboth high spectral and spatial performance in terms of quality indices and visualeffect effectively.
引文
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