【摘要】：隨著遙感技術(shù)的發(fā)展，其應(yīng)用也越來越廣泛，在地學(xué)科學(xué)、農(nóng)業(yè)、氣象、林業(yè)、城市規(guī)劃、環(huán)境監(jiān)測等等領(lǐng)域均有不同程度的應(yīng)用。然而，由于遙感傳感器技術(shù)本身的限制，所獲得的遙感數(shù)據(jù)往往不能反映出區(qū)域的全部信息。為了更好的理解該地域的內(nèi)容，將不同遙感器獲得的圖像信息進(jìn)行融合便成了一項(xiàng)十分經(jīng)濟(jì)且有效的方案。近年來，為了提高對遙感圖像的解譯能力，信息融合的技術(shù)被引入到融合多遙感器圖像及遙感衛(wèi)星圖像中。 本論文以遙感圖像融合為研究背景，結(jié)合國家自然科學(xué)基金、國家“863”計(jì)劃、“973”計(jì)劃以及“111”創(chuàng)新引智計(jì)劃等項(xiàng)目的任務(wù)與需求，利用多尺度幾何分析、機(jī)器學(xué)習(xí)方法和優(yōu)化算法等工具，完成了遙感圖像融合方法的研究工作。論文主要工作概括如下： 1.借助支撐矢量機(jī)逼近原理對圖像進(jìn)行逼近建模，由此實(shí)現(xiàn)以核函數(shù)來描述圖像，并提出一種多尺度變換工具——小波核濾波器。將小波核濾波器應(yīng)用在橋梁分類、移動和靜止目標(biāo)獲取和識別數(shù)據(jù)庫（Moving and Stationary TargetAcquisition and Recognition，MSTAR）的數(shù)據(jù)識別及合成孔徑雷達(dá)圖像去斑中。橋梁分類和MSTAR數(shù)據(jù)識別應(yīng)用結(jié)果表明，圖像數(shù)據(jù)經(jīng)過小波核濾波器之后得到系數(shù)能更好的表達(dá)原圖像的信息，而圖像去斑的應(yīng)用也顯示出由于該濾波器具有平移不變性，針對去斑中出現(xiàn)的振鈴效應(yīng)基本清除。 2.將提出的小波核濾波器應(yīng)用到遙感圖像融合中。小波核濾波器具有多尺度性、平移不變性、完全重構(gòu)性等，使得該濾波器在圖像融合的應(yīng)用中具有優(yōu)勢。針對多傳感器圖像的特點(diǎn)，以區(qū)域能量最大值作為融合策略，實(shí)現(xiàn)基于小波核濾波器的多傳感器圖像融合，并與其他多尺度變換工具如小波變換、非下采樣的小波變換、Contourlet變換、非下采樣的Contourlet變換進(jìn)行比較。四組針對曼徹斯特大學(xué)圖像融合庫的多源圖像融合結(jié)果表明，小波核濾波器應(yīng)用于多傳感器圖像融合是有效的，克服了圖像融合中常出現(xiàn)的振鈴效應(yīng)，細(xì)節(jié)保持較好，取得更為清晰的融合結(jié)果。針對多光譜與全色圖像的融合問題，在小波核濾波器的基礎(chǔ)上，提出兩種融合策略：其一是與傳統(tǒng)的亮度-色調(diào)-飽和度變換相結(jié)合，對亮度I分量進(jìn)行處理，，將全色圖像的細(xì)節(jié)加入到I分量中；其二是采用改進(jìn)的空間分辨率增加框架法（Amélioration de laRésolution Spatiale par Injection de Structures，ARSIS）作為融合框架，利用多尺度分析手段為多光譜圖像補(bǔ)充上缺失的細(xì)節(jié)成分。隨后給出的來自于光學(xué)衛(wèi)星的多光譜圖像結(jié)果表明，小波核濾波器能夠應(yīng)用在多光譜圖像與全色圖像的融合中，獲取融合結(jié)果，兩種融合框架均能獲得所需的具有高分辨率的多光譜圖像，為后續(xù)多光譜圖像的處理及應(yīng)用奠定了基礎(chǔ)。 3.針對遙感圖像融合問題，提出了小波核濾波器結(jié)合優(yōu)化算法的遙感圖像融合方法。首先，結(jié)合小波核濾波器，將粒子群算法應(yīng)用到多傳感器圖像融合中。針對細(xì)節(jié)子帶仍采用區(qū)域能量最大值的融合策略，而近似子帶則選擇粒子群算法去搜索得到一個(gè)最優(yōu)的近似子帶。實(shí)驗(yàn)結(jié)果表明，結(jié)合小波核濾波器和粒子群算法的方法是有效的，可以得到相對最優(yōu)的融合結(jié)果。針對多光譜與全色圖像的融合問題，結(jié)合小波核濾波器和克隆選擇算法給出兩種融合策略：其一是通過小波核濾波器中參數(shù)的變化，給出多組小波核濾波器，結(jié)合亮度-色調(diào)-飽和度變換獲得多組融合結(jié)果，克隆選擇算法用來尋找到最優(yōu)權(quán)值組合給出最優(yōu)融合結(jié)果；其二是利用克隆選擇算法尋找最優(yōu)的亮度I分量，得到一個(gè)最逼近全色圖像的I分量進(jìn)行隨后的融合處理。結(jié)果表明，結(jié)合優(yōu)化算法的融合策略能夠找到最優(yōu)值，得到相對最優(yōu)的融合結(jié)果。 4.隨著稀疏表示理論的發(fā)展，該理論已被成功的應(yīng)用于圖像處理領(lǐng)域中。由于圖像能夠采取稀疏表示的方式來得到系數(shù)，稀疏的系數(shù)用來表達(dá)源圖包含的信息，因此利用該系數(shù)便可完成圖像融合的要求。根據(jù)多源圖像的特點(diǎn)及稀疏表示獲得的稀疏系數(shù)的特點(diǎn)，給出五種融合策略下得到的融合結(jié)果，并進(jìn)行了比較，選擇出適合于稀疏系數(shù)的融合規(guī)則，并與傳統(tǒng)的多尺度變換方法進(jìn)行比較，結(jié)果表明稀疏表示理論應(yīng)用到圖像融合領(lǐng)域亦能獲得較優(yōu)的結(jié)果。針對多光譜圖像融合的問題，首先參照多光譜圖像的特點(diǎn)，將基于稀疏表示的超分辨方法應(yīng)用到多光譜圖像與全色圖像的融合中，通過超分辨方法先獲得對應(yīng)于低分辨率多光譜圖像的高分辨率圖像，結(jié)合ARSIS框架與全色圖像融合，獲得了較好的結(jié)果。 5.考慮到多光譜圖像融合的目的在于增加多光譜圖像的細(xì)節(jié)信息含量，將二維經(jīng)驗(yàn)?zāi)Ｊ椒纸庖氲角拔奶岬降幕趶V義的亮度色度飽和度變換和小波核濾波器結(jié)合的多光譜圖像融合方法中，并且為了找到既能提高光譜性又增加細(xì)節(jié)信息的結(jié)果，將折中參數(shù)引入了融合方法中，由此獲取高分辨率多光譜圖像，使得新獲取的圖像能夠在保持光譜特性的基礎(chǔ)上增加盡可能多的細(xì)節(jié)信息。
[Abstract]:With the development of remote sensing technology, its application is becoming more and more extensive, and there are different applications in the fields of geoscience, agriculture, meteorology, forestry, urban planning, environmental monitoring and so on. However, due to the limitations of the remote sensing sensor technology itself, the acquired remote sensing data often cannot reflect all of the information of the region. In order to better understand the content of the region, the fusion of the image information obtained from the different remote sensors is a very economical and effective solution. In recent years, in order to improve the interpretation capability of the remote sensing image, the information fusion technology is introduced into the integrated multi-sensor image and the remote sensing satellite image. Based on the research background of remote sensing image fusion, this paper makes use of multi-scale geometric analysis, machine learning method and optimization algorithm in combination with the task and demand of national natural science fund, national "863" plan, "973" plan and "111" innovation and intelligence program. The study of the method of remote sensing image fusion is completed. The main work of the paper is as follows: In that follow:1. the image is approximated by means of the approximation principle of the support vector machine, and the kernel function is used to describe the image, and a multi-scale transform tool _ wavelet kernel is proposed. Filter. Application of wavelet kernel filter in data recognition and synthetic aperture radar image of bridge classification, mobile and stationary target acquisition and recognition database (MSTAR) The results of the application of the bridge classification and the MSTAR data recognition show that the image data can express the information of the original image better after passing through the wavelet kernel filter, and the application of the image despot also shows that the filter has the translation Invariance, the ringing effect base that appears in the spot-to-spot this cleanup.2. Applying the proposed wavelet kernel filter to remote sensing In image fusion, wavelet kernel filter has multi-scale, translational invariance, complete reconstruction and so on, so that the filter is used in image fusion The multi-sensor image fusion based on the wavelet kernel filter is realized based on the characteristic of the multi-sensor image and the maximum value of the regional energy is taken as a fusion strategy, let transform, non-downsampled Contourlet change The results show that the wavelet kernel filter is effective in multi-sensor image fusion, and the ringing effect in the image fusion is overcome. In order to solve the fusion problem of multi-spectrum and full-color image, two fusion strategies are proposed on the basis of wavelet kernel filter: one is combined with the traditional brightness-tone-saturation transformation, and the brightness I component is processed, and the detail of the full-color image is added. The second is to use the improved spatial resolution adding frame method (ARSIS) as the fusion frame, and the multi-scale analysis is used to supplement the multi-spectral image. The results of the multi-spectral images from the optical satellite show that the wavelet kernel filter can be used in the fusion of the multi-spectral image and the full-color image to obtain the fusion result, and the two fusion frames can obtain the required high resolution. Multi-spectral image, processing and application of subsequent multi-spectral image In order to solve the problem of remote sensing image fusion, a method of combining the wavelet kernel filter with the optimization algorithm is proposed. The invention relates to a sensing image fusion method, which comprises the following steps of: firstly, applying a particle swarm algorithm to a multi-pass filter in combination with a wavelet kernel filter, In the image fusion, the fusion strategy of the maximum value of the region energy is still used for the detail sub-bands, while the approximate sub-bands select the particle swarm algorithm to search for one. The experimental results show that the method of combining wavelet kernel filter and particle swarm optimization is effective and can be obtained. In order to solve the fusion problem of multi-spectrum and full-color image, two fusion strategies are given in combination with the wavelet kernel filter and the clonal selection algorithm. a group wavelet kernel filter is combined with the brightness-tone-saturation transformation to obtain a plurality of sets of fusion results, the cloning selection algorithm is used for finding the optimal weight combination to give the optimal fusion result, The optimal brightness I component is obtained by obtaining an I component of the most approximate full-color image. The result shows that the optimal value can be found with the fusion strategy of the optimization algorithm to get the relative value. 4. With the development of sparse representation theory, the theory has been successfully in that field of image proces, since the image is able to obtain a coefficient in a sparse representation, the sparse coefficient is used to express the information contained in the source map, according to the characteristics of the multi-source image and the characteristic of the sparse coefficient obtained by the sparse representation, the fusion result obtained under the five fusion strategies is given, the fusion rule suitable for the sparse coefficient is selected, The results show that the sparse representation theory is applied in the field of image fusion. in order to solve the problem of multi-spectral image fusion, firstly, the super-resolution method based on the sparse representation is applied to the fusion of the multi-spectral image and the full-color image by referring to the characteristic of the multi-spectral image, High-resolution image of image, combined with ARSIS frame and full-color image fusion 5. The aim of the multi-spectral image fusion is to increase the detail information content of the multi-spectral image, and the two-dimensional empirical mode decomposition is introduced into the above-mentioned generalized luminance-chroma saturation transform and the wavelet kernel filter combination. In the multi-spectral image fusion method, and in order to find the result that both the spectral property and the detail information can be improved, the compromise parameter is introduced into the fusion method, thereby obtaining a high-resolution multi-spectral image, so that the newly acquired image can be on the basis of keeping the spectral characteristic,
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TP751

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