【作者】 李行；

【导师】 张连蓬；

【作者基本信息】 山东科技大学 ， 地图制图学与地理信息工程， 2006， 硕士

【摘要】 传感器技术的发展，使得收集上百个从可见光到近红外的连续的光谱波段的数据成为现实。波段数的增加，一方面提供了潜在的区分细微地物类别的能力；另一方面，由于可获得的训练样本数无法满足高维数据分类器参数估计精度的要求，导致分类器性能随着额外波段的加入而下降。这两个方面为我们提出了不同的问题。其一要求我们利用降维方法，提取或选择对分类更有利的波段，其二希望我们找到能够充分利用和发掘高光谱影像数据优势与潜力的处理方法。因此，本文首先对作为降维方法之一的波段选择理论进行了较为系统的研究，包括快速算法、评价方案和不同波段选择方法间的比较分析等不同的层次。然后基于高光谱影像数据具有更好的类别可分性这一前提，对易混分地类问题进行了探索性的研究。 具体内容如下。首先，我们叙述了几种常用的波段选择方法，包括基于信息量的波段选择方法和基于类别可分性的波段选择方法(第二章)；然后，分析了常用的波段选择方法在编程实现中的具体问题，提出了波段选择的快速算法和一种有效的波段选择算法的评价方案(第三章)；接下来设计了三组试验，应用本文提出的快速算法和评价方法对几种常用的波段选择方法进行了比较研究，得到了一些指导性的结论同时也进一步验证了快速算法和评价方法的有效性(第四章)；最后，针对易混分地类问题，提出了地类混淆指数(CCI)作为一种估算地类混分程度的简单方法，详细论述了类别可分性距离量度指标的理论前提和它们之间的关系，在此基础上设计了两种面向易混分地类的波段选择方案，经过试验比较得到了理想的结果(第五章)。更多还原

【Abstract】 Rapid advances in sensor technology have made it recently possible to collect hyperspectral remote sensing data of hundreds of contiguous spectral bands which spans typically from visible light to near infrared energy range. Such high dimensionality, on one hand, provides us with more potential discrimination power between classes with subtle differences. On the other hand, it is more difficult to get better classification performance, due to the fact that the available number of training samples is unable to catch up with the increase of dimensionality, which causes poor estimation accuracy for classifiers’ parameters. Maybe, we need to strive to make two aspects of tasks. The one is that using dimensionality reduction techniques to select or extract bands subset from original data, which can improve classifier’s performance. The other is that to develop special methods of image processing which could employ the potentials and advantages of hyperspectral remote sensing data. So, firstly we study systematically the band selection theory, which is one of dimensionality reduction methods, including a fast algorithm, evaluation method, and a comparative study in this dissertation. And then, based on higher dimensionality with better separability, we make heuristic study as to easy-mixed class problem, and achieve some satisfied results by experiments.The concrete contents of this dissertation are the following. To begin with, we recite some band selection methods developed by other researchers, comprising content-based methods and separability-based methods (Chapter 2). Then, we analysis their characteristics and furthermore propose a fast algorithm and develop an evaluation method for band selection algorithms (Chapter 3). And then, a comparative study is implemented by design three experiments with utilizing the fast algorithm and evaluation method (Chapter 4). Lastly, aim at the easy-mixed class problem, we propose class confusion index (CCI) to decide the confusion degree between classes. The index is simpler and more precise than the confusion matrix. It can provide significant guidance for pre-processing step before classification. As an important discriminant criterion of separability, several class separability measures, such as divergence, Mahalanobis distance, Chernoff Distance, and Bhattacharyya Distance, etc., are deduced更多还原