基于稀疏图的小样本高光谱图像半监督分类算法研究

发布时间：2018-02-28 15:09

本文关键词： 半监督分类高光谱图像 DL1图 KNN图标记传播　出处：《北方民族大学》2017年硕士论文　论文类型：学位论文

【摘要】：当前,机器学习的相关理论和应用研究遍地开花。传统机器学习常用的两种方法为无监督学习和有监督学习。然而我们也应该看到,无监督学习的特点和优势是不需要训练样本,但无监督学习对于空间分布较复杂的数据难以得到好的学习效果;另一方面,有监督学习虽然学习性能较好,但当训练样本数目很少时,有监督学习难以准确学习出样本的真实分布。由于半监督学习可以利用大量的未标记样本来辅助有限的有标记样本,从而避免无监督学习和有监督学习两种方法的弊端,结合其优势,因而半监督学习可以提高学习的准确性。由于半监督学习可以在得到较高分类精度的同时降低学习的成本,它对在理论和实际中提高学习器的分类性能有着非常重要的指导意义。在半监督学习的众多方法中,基于图论的半监督学习方法在实践中取得了实证性的成功,它成为了半监督学习方法中最流行的一种。但是,基于图的半监督学习方法有其特殊性,构图方法会对学习器的性能产生较大影响。针对构图问题,我们提出一种基于可区分L1范数图和KNN图叠加图的半监督分类算法。并将该算法用于小样本高光谱图像分类问题上。本文的主要研究工作如下:基于少数已标记样本的高光谱图像分类是一个具有挑战的任务。对于基于图的方法,如何构造图是分类成功的关键。本文提出一种新的构图方法,首先在L1范数图基础上构造一个区分能力更强的L1图,即DL1图,并将其和KNN图叠加,在半监督框架下来解决高光谱图像的分类问题。本文的图构造方法包括两个步骤,首先,使用稀疏表示方法估计任意两个像素属于同一类别的概率,构建相应概率矩阵,然后再将概率矩阵整合到L1图中,从而得到DL1图。其次,将DL1图与KNN图以一定比例线性叠加。在Indiana Pines高光谱数据集上的实验表明,所提方法的分类识别率更高。本文将概率矩阵与L1图的权值矩阵叠加,形成了强鉴别力的DL1图。将空间的局部信息与光谱的全局信息通过KNN图和DL1图结合在一起,构建了空谱信息联合的图框架结构,使用该框架构建的图,能更精细的反映高光谱图像数据的图谱结构。利用图的标记传播达到半监督分类的目的,以此提高小样本高光谱图像自动分类的精度,实验表明,在标记样本比例为5%时,分类精度提升亦非常显著。
[Abstract]:At present, machine learning theory and Application Research of traditional machine learning. Blossom everywhere the two commonly used methods for unsupervised learning and supervised learning. However, we should also see that without the characteristics and advantages of supervised learning is not required for the training samples, but unsupervised learning for the distribution of data is complex it is difficult to get good learning effect; on the other hand, supervised learning is better learning performance, but when the number of training samples is small, supervised learning is difficult to learn the true distribution of the sample. The semi supervised learning can use unlabeled samples to assist limited labeled samples, so as to avoid the disadvantages of unsupervised learning and supervised learning two methods, combined with its advantages, so the semi supervised learning can improve the learning accuracy. Because semi supervised learning can get higher classification precision and lower The cost of learning, it has a very important significance to improve the classification performance of learning in theory and practice. Many methods in the semi supervised learning, semi supervised learning method based on graph theory has achieved substantial success in practice, it has become one of the most popular methods in the semi supervised learning however, graph based semi supervised learning method has its particularity, patterning method will have great influence on the performance of classifier. Aiming at the problem of composition, we propose a discriminative semi supervised classification algorithm based on L1 norm and KNN diagram based on superposition graph. And the algorithm for hyperspectral image classification problems. The main research work of this paper is as follows: hyperspectral image classification based on few labeled samples is a challenging task for graph based methods, how to construct a map is the key to success. This paper proposes a classification A new method of composition, a stronger ability to distinguish L1 graph constructed in L1 norm map based on DL1 map and KNN map, and the overlay, solve the classification problem of hyperspectral image in the semi supervised framework. The graph construction method includes two steps: firstly, using sparse representation method the estimated probability of any two pixels belonging to the same category, construct the corresponding probability matrix, and then the probability matrix is integrated into the L1 map, DL1 map is obtained. Secondly, the DL1 map and KNN map to a certain proportion of linear superposition. Show in Indiana Pines hyperspectral data set on the experiment, the proposed method of classification and recognition the rate is high. The weight matrix superposition probability matrix and L1 map, the formation of a strong discrimination DL1. The global information and local information of spectral space by KNN map and DL1 map together, constructs the space spectrum framework of information combination The structure, the use of the framework construction of the map, which can reflect the structure of hyperspectral image data is more accurate. To achieve semi supervised classification using marker propagation graph for the purpose, in order to improve the classification accuracy of hyperspectral images of small samples, experiments show that, in the proportion of labeled samples is 5%, enhance the classification accuracy is also very significant.

【学位授予单位】：北方民族大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41;TP181

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