英语翻译摘要高光谱遥感影像具有数十至数百个各波段的数据,为人们了解地物提供了丰富的信息,这对地物的分类和目标的识别是十分有利的.高光谱遥感数据记录了地物目标的连续光谱.和其

英语翻译摘要高光谱遥感影像具有数十至数百个各波段的数据,为人们了解地物提供了丰富的信息,这对地物的分类和目标的识别是十分有利的.高光谱遥感数据记录了地物目标的连续光谱.和其
英语翻译
摘要
高光谱遥感影像具有数十至数百个各波段的数据,为人们了解地物提供了丰富的信息,这对地物的分类和目标的识别是十分有利的.高光谱遥感数据记录了地物目标的连续光谱.和其他类型的遥感影像相比,信息量更丰富,因而具备了识别更多种类的地物目标,以及以更高的精度进行目标分类的能力.但是,由于高光谱遥感影像数据量大,且存在数据冗余的现象,目前在对其进行分类时,常常需要先对数据进行降维,再利用其他一些分类算法分类.这样做增加了工作量,整个分类过程未能实现“一体化”、“自动化”.
决策树算法具有灵活、直观、清晰、强健、运算效率高等特点,在遥感影像分类领域值得研究.目前比较成熟的决策树算法有ID3算法、C4.5算法、CART算法、C5.0算法等,它们比较适用于离散型的数据集的分类,且处理的数据集大小有限,因此用来处理遥感数据就存在着一定的不足,需研究一种适合于遥感影像分类的决策树.
将决策树分类技术与高光谱遥感数据分类相结合,可以利用决策树的优势,充分挖掘高光谱数据中最有用的信息,对于研究高光谱遥感影像分类新方法,对于提高地物目标的识别能力和分类精度,都具有重大的理论和实际意义.本文研究了基于决策树的高光谱遥感影像分类方法,提出了一种决策树自动构建算法.试验通过对高光谱遥感影像进行现场采样,对样本进行训练,从而自动生成一棵二叉分类决策树,并对影像进行分类.决策树在每个决策节点处选择最具优势的波段和阈值,整棵树简单明了,分类规则易于理解,分类效率和精度都比较高.实现了高光谱遥感影像从数据降维、样本选择、样本训练、决策树生成、影像分类的“一体化”和“自动化”.
关键词：二叉决策树,高光谱遥感影像分类,均值间标准距离,最佳阈值,自动构建

英语翻译摘要高光谱遥感影像具有数十至数百个各波段的数据,为人们了解地物提供了丰富的信息,这对地物的分类和目标的识别是十分有利的.高光谱遥感数据记录了地物目标的连续光谱.和其
abstract
Hyperspectral remote sensing images with dozens of to hundreds of each band of data, for people to understand the features provides abundant information, the classification and features of recognition is very favorable targets. Hyperspectral remote sensing data recorded the continuous spectrum of terrain target. And other types of remote sensing image, more information than rich, consequently have recognition more kinds of geophysics target with higher precision, and the ability of target classification. But hyperspectral remote sensing images, because of large amount of data, and there is a data redundancy and is currently in the phenomenon of its classification, often need first to data dimension reduction, reuse some other classification algorithm classification. To do so, the increased workload sorting failed to achieve "integration", "automatic".
The decision tree algorithm has flexible, intuitive, clear, strong, operational efficiency higher characteristic, in remote sensing image classification field is worth studying. At present more mature decision tree algorithm has ID3 algorithm, C4.5 algorithm, CART algorithm, C5.0 algorithm, etc, they are suitable for discrete data sets the classification, and processing data sets size is limited, so used to deal with remote sensing data is there are some deficiencies, need to study a suitable for remote sensing image classification decision tree.