【摘要】：隨著成像和處理技術(shù)的進步,高光譜遙感在地質(zhì)勘探、軍事應(yīng)用、植被檢測、海洋遙感等領(lǐng)域發(fā)揮著越來越重要的作用。但是由于儀器空間分辨率的局限性和地球表面結(jié)構(gòu)的復(fù)雜多樣性,圖像中的一個像元往往包含著多種不同的地物類型,從而形成了混合像元。 “端元”是高光譜圖像中能詳盡表示待測地物光譜屬性的純像素,可以作為后續(xù)高光譜圖像處理算法的先驗知識。獲取能夠很好地反映待研究地物光譜屬性信息的端元向量,是對高光譜數(shù)據(jù)做進一步分析的重要前提。N-FINDR是一個經(jīng)典且有效的端元尋找算法,能夠在保證豐度約束性的同時,獲取圖像的實際像元作為端元,對后續(xù)的分類、識別和解混等作用顯著。但理想的N_FINDR算法需要遍歷所有可能的像元組合,計算量巨大。而目前用于加快搜索速度的算法,其最終結(jié)果大多在一定程度上受到樣本排序和初始端元集選擇的影響。另外,目前所關(guān)注的端元主要包括如軍事目標(biāo)探測中的異常點,以及圖像組分分析中的大面積成分端元點。在進行圖像主要組分分析時,過多無關(guān)端元的參與會降低解混和分類的精度,但目前的端元尋找算法大多并未對這兩類端元進行區(qū)分。 論文的研究工作主要包括以下三部分：第一,初始端元集的優(yōu)化。利用基于相關(guān)性分析的N-FINDR算法獲取端元集作為初始端元集,降低初始端元間的關(guān)系及其對最終結(jié)果的不利影響；第二,利用粒子群算法進一步優(yōu)化候選端元。對于與初始端元相關(guān)性系數(shù)大于某一閾值的所有像元向量進行粒子群優(yōu)化,以保證選出更接近于真實端元的像元作為最終端元。第三,利用端元變異性定義粒子群算法的優(yōu)化目標(biāo)。定義新的目標(biāo)函數(shù)為以每個候選端元為類別中心的Fishier比,即以單形體體積作為類間變異,閾值內(nèi)像元的方差為類內(nèi)變異。選擇能夠最大化該Fishier比值的像元為端元,實現(xiàn)了對異常點(包括噪聲點)的抑制。最后,利用模擬數(shù)據(jù)生成混合像元的影像,驗證改進N-FINDR算法的有效性。 以海上溢油檢測和分析為問題背景,關(guān)心的主要目標(biāo)組分是油、水和船。利用真實的機載海上溢油圖像對論文中的算法進行測試,結(jié)果進一步驗證了論文所提出算法的有效性。
[Abstract]:With the development of imaging and processing technology, hyperspectral remote sensing plays a more and more important role in geological exploration, military applications, vegetation detection, marine remote sensing and other fields. However, due to the limitation of the spatial resolution of the instrument and the complex diversity of the earth's surface structure, a pixel in the image often contains many different types of ground objects, thus forming a mixed pixel. "end element" is a pure pixel in hyperspectral image, which can represent the spectral properties of the object to be tested in detail, and can be used as a priori knowledge of the subsequent hyperspectral image processing algorithm. Obtaining the end element vector which can well reflect the spectral attribute information of the ground object to be studied is an important prerequisite for further analysis of hyperspectral data. N-FINDR is a classical and effective end element search algorithm. It can not only ensure the abundance constraint, but also obtain the actual pixel of the image as the end element, which plays an important role in subsequent classification, recognition and unmixing. However, the ideal N_FINDR algorithm needs to traverse all possible pixel combinations, and the amount of computation is huge. At present, the algorithm used to speed up the search speed, the final results are mostly affected by the sample sorting and the selection of the initial set of terminal elements to a certain extent. In addition, the end elements concerned at present mainly include abnormal points in military target detection and large area component end points in image component analysis. In the analysis of the main components of the image, the participation of too many unrelated end elements will reduce the accuracy of unmixing and classification, but most of the current end element search algorithms do not distinguish between the two kinds of end elements. The research work of this paper mainly includes the following three parts: first, the optimization of the initial set of elements. The N-FINDR algorithm based on correlation analysis is used to obtain the end element set as the initial end element set to reduce the relationship between the initial end elements and their adverse effects on the final result. Secondly, the particle swarm optimization algorithm is used to further optimize the candidate terminal elements. Particle swarm optimization is carried out for all pixel vectors whose correlation coefficient with the initial end element is greater than a certain threshold in order to ensure that the pixel which is closer to the real end element is selected as the final end element. Third, the end element variability is used to define the optimization objective of particle swarm optimization algorithm. The new objective function is defined as the Fishier ratio with each candidate terminal element as the category center, that is, the volume of the single body is taken as the inter-class variation, and the variance of the pixel within the threshold is the intra-class variation. The pixel which can maximize the Fishier ratio is selected as the end element, and the outliers (including noise points) are suppressed. Finally, the simulation data are used to generate mixed pixel images to verify the effectiveness of the improved N-FINDR algorithm. Based on the detection and analysis of offshore oil spills, the main target components are oil, water and ships. The real airborne offshore oil spill image is used to test the algorithm in this paper, and the results further verify the effectiveness of the proposed algorithm.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP751

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