本文選題：高光譜遙感 + 稀疏解混　； 參考：《北方民族大學》2017年碩士論文

【摘要】：高光譜遙感圖像既含有大量的空間信息,還含有充裕的光譜信息,是遙感領域近年來的研究熱點。然而,在實際的高光譜圖像中,由于傳感器空間分辨率的限制和自然界地物的復雜性,單個像元通常聚集了多種特征地物,它們依據(jù)某種比例混合而成,形成混合像元�；旌舷裨拇嬖谧璧K了高光譜圖像解釋、目標識別以及分類,這就要求解混技術的出現(xiàn)。稀疏解混是高光譜遙感數(shù)據(jù)分解中常用的線性光譜解混工具,它通過利用預先得到的光譜庫完成解混,屬于一種半監(jiān)督的方式。過去,大多數(shù)稀疏回歸方法都是基于凸松弛的,其試圖得到明確定義的優(yōu)化問題的全局解。近來,由于對低計算復雜度的需求,越來越多的人開始關注稀疏約束的貪婪解混算法,其中,子空間匹配追蹤(SMP)依據(jù)原始圖像的不同列迭代地提取最佳端元,是目前表現(xiàn)較好的算法。本文研究與總結了混合像元分解的相關技術,針對真實光譜圖像受噪聲所影響嚴重的現(xiàn)象,在已有稀疏解混算法的基礎上,借鑒了幾種成熟相似度計算方法并把它們的優(yōu)缺點進行簡單分析對比,提出了SMP稀疏解混的改進算法,用Dice系數(shù)法替代內(nèi)積法作為新的匹配準則,通過計算所有光譜信號的算術平均值,考慮了端元光譜自身的信息,而不僅僅是殘差與光譜庫的相關度信息,此外,本文還添加一個預分塊策略,規(guī)避了端元數(shù)量很大時算法陷入局部最優(yōu)的問題,同時也更好的利用了空間信息。
[Abstract]:Hyperspectral remote sensing images not only contain a large amount of spatial information, but also contain abundant spectral information, which is a research hotspot in the field of remote sensing in recent years. However, in the actual hyperspectral images, due to the limitation of sensor spatial resolution and the complexity of natural features, a single pixel usually gathers a variety of feature features, which are mixed according to a certain proportion to form mixed pixels. The existence of mixed pixels hinders the emergence of hyperspectral image interpretation, target recognition and classification. Sparse demultiplexing is a commonly used linear spectral descrambling tool in hyperspectral remote sensing data decomposition. It is a semi-supervised method by using the pre-acquired spectral database. In the past, most sparse regression methods were based on convex relaxation, which attempted to obtain the global solution of the well-defined optimization problem. Recently, due to the demand for low computational complexity, more and more people begin to pay attention to the greedy demultiplexing algorithm with sparse constraints, in which subspace matching tracking (SMP) iteratively extracts the best endpoints according to the different columns of the original image. Is the current performance of the better algorithm. In this paper, the related techniques of mixed pixel decomposition are studied and summarized. Aiming at the phenomenon that the real spectral image is seriously affected by noise, based on the existing sparse demultiplexing algorithms, Several mature similarity calculation methods are used for reference and their advantages and disadvantages are simply analyzed and compared. An improved algorithm for sparse demultiplexing of SMP is proposed. The Dice coefficient method is used instead of the inner product method as a new matching criterion. By calculating the arithmetic mean of all spectral signals, the information of endmember spectrum itself, not only the correlation information between residual and spectral database, is considered. In addition, a preblocking strategy is added in this paper. It avoids the problem of local optimization when the number of endelements is large, and makes better use of spatial information.
【學位授予單位】：北方民族大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP751

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本文編號：2080993