發(fā)布時間：2018-04-04 07:03

  本文選題：高光譜圖像　切入點：松弛變量　出處：《大連海事大學》2017年碩士論文

【摘要】：高光譜遙感技術提供一種光譜解混的方法來分析混合像元中組成成分以及所占的比例。比較經典的算法是最小二乘算法,但該算法對豐度值沒有任何約束,這是不符合實際物理意義的。全約束豐度估計算法同時滿足非負性和和為一約束,具有實際物理意義。由于全約束豐度估計物理意義合理,近幾年被廣泛用于光譜解混算法中,但是端元數目過大時,該算法的工作效率降低,且當提取的端元數量不完全以及端元不理想時,解混誤差也會變大。在提高算法的效率方面,已有部分改進工作,但是在提高解混精度方面的研究相對較少。本文基于傳統(tǒng)的原始-對偶內點算法,考慮地物的豐度統(tǒng)計特征以及高光譜圖像成像復雜、普遍存在噪聲的特點,提出了兩種新的對目標函數進行約束的原始-對偶內點算法。首先,本文在傳統(tǒng)原始-對偶內點算法上進行改進,通過改進選擇步長參數來使迭代出的點位于原始-對偶中心路徑上,而不是通過路徑跟蹤法去計算中心路徑。另外,在進行移動方向計算前,需要進行對偶間隙中參數的估計,這樣可以保證原問題和對偶問題均趨于最優(yōu)解。其次,因為高光譜圖像存在空間分辨率低、噪聲普遍、地物復雜、端元數量未知、可能不存在純端元等問題,使豐度和為一約束不再滿足。因此,本文通過加入松弛變量,來控制豐度和為一約束性。提出了結合松弛變量和改進選擇步長參數的原始-對偶內點算法,即松弛原始-對偶內點算法�；�于以上理論的研究,論文完成了對上述算法的理論和優(yōu)化過程推導,分別在模擬高光譜圖像和真實高光譜上對提出的算法進行實驗,驗證了所推出的算法在豐度估計準確度和重構誤差上都得到了較原算法更好的精度,且在端元個數未知且端元不理想時,依然能夠獲得穩(wěn)定的解混結果。
[Abstract]:Hyperspectral remote sensing provides a spectral demultiplexing method to analyze the composition and proportion of the mixed pixel.The classical algorithm is the least square algorithm, but the algorithm has no restriction on the abundance value, which is not in line with the actual physical meaning.The full constraint abundance estimation algorithm satisfies the sum of nonnegative sum and is a constraint, which is of practical physical significance.Due to the reasonable physical meaning of the fully constrained abundance estimation, it has been widely used in the spectral demultiplexing algorithm in recent years. However, when the number of endmembers is too large, the efficiency of the algorithm is reduced, and when the number of the extracted endmembers is incomplete and the endmembers are not ideal,The unmixing error also increases.In the aspect of improving the efficiency of the algorithm, some improvements have been done, but the research on improving the resolution of the algorithm is relatively few.Based on the traditional primal-dual interior point algorithm and considering the statistical feature of the abundance of the ground object and the complexity of hyperspectral image imaging and the ubiquitous noise, two new primal-dual interior point algorithms are proposed to constrain the objective function.Firstly, this paper improves the traditional primal-dual interior point algorithm. By improving the step size parameter, the iterative point is located on the primal-dual center path, rather than the path tracking method to calculate the central path.In addition, it is necessary to estimate the parameters in the duality gap before calculating the moving direction, which can ensure that both the original problem and the dual problem tend to the optimal solution.Secondly, because of the problems of low spatial resolution, universal noise, complex objects and unknown number of endmembers in hyperspectral images, there may be no pure endpoints, so that the sum of abundance is no longer satisfied with a constraint.Therefore, the abundance sum is controlled by the addition of relaxation variables.A primal-dual interior point algorithm, a relaxation primal-dual interior point algorithm, is proposed, which combines relaxation variables with improved selection of step size parameters.Based on the above theoretical research, this paper has completed the theoretical and optimization process derivation of the above algorithm, respectively in the simulation of hyperspectral images and real hyperspectral experiments on the proposed algorithm.It is verified that the proposed algorithm has better accuracy than the original algorithm in terms of accuracy of abundance estimation and reconstruction error, and can still obtain stable unmixing results when the number of endmembers is unknown and the endmembers are not ideal.
【學位授予單位】：大連海事大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP751

【參考文獻】

相關期刊論文 前7條

1 童慶禧;張兵;張立福;;中國高光譜遙感的前沿進展[J];遙感學報;2016年05期

2 宋梅萍;張甬榮;安居白;包海默;;基于有效端元集的雙線性解混模型[J];光譜學與光譜分析;2014年01期

3 普晗曄;王斌;張立明;;基于單形體幾何的高光譜遙感圖像解混算法[J];中國科學:信息科學;2012年08期

4 李二森;朱述龍;周曉明;余文杰;;高光譜圖像端元提取算法研究進展與比較[J];遙感學報;2011年04期

5 王立國;鄧祿群;張晶;;基于線性最小二乘支持向量機的光譜端元選擇算法[J];光譜學與光譜分析;2010年03期

6 吳柯;張良培;李平湘;;一種端元變化的神經網絡混合像元分解方法[J];遙感學報;2007年01期

7 錢樂祥,泮學芹,趙芊;中國高光譜成像遙感應用研究進展[J];國土資源遙感;2004年02期

相關博士學位論文 前3條

1 楊華東;高光譜遙感影像光譜解混算法研究[D];大連海事大學;2015年

2 李二森;高光譜遙感圖像混合像元分解的理論與算法研究[D];解放軍信息工程大學;2011年

3 黃遠程;高光譜影像混合像元分解的若干關鍵技術研究[D];武漢大學;2010年

相關碩士學位論文 前1條

1 肖霄;基于正交投影理論的端元提取算法[D];大連海事大學;2016年

，

本文編號：1708877