1、2500 单词， 3900 汉字 出处： Du P, Tao F, Hong T. Spectral Features Extraction in Hyperspectral RS Data and Its Application to Information Processing *J+. Acta Photonica Sinica, 2005, 34(2):293 -298. 本科毕业设计（论文） 中英文对照翻译 院（系部） 测绘与国土信息工程学院 专业名称 测绘工程 年级班级 学生姓名 指导老师 2012 年 6 月 3 日 1 Spectral Features Extraction
2、in Hyperspectral RS Data and Its Application to Information Processing Oriented to the demands of hyperspectral RS information processing and applications, spectral features in hyperspectral RS image can be categorized into three scales: point scale, block scale and volume scale. Based on the proper
3、ties and algorithms of different features, it is proposed that point scale features can be divided into three levels: spectral curve features, spectral transformation features and spectral similarity measure features. Spectral curve features include direct spectra encoding, reflection and absorption
4、 features. Spectral transformation features include Normalized Difference of Vegetation Index (NDV I) , derivate spectra and other spectral computation features. Spectral similarity measure features include spectral angle ( SA ) , Spectral Information Divergence ( SID ) , spectral distance, correlat
5、ion coefficient and so on. Based on analysis to those algorithms, several problems about feature extraction, matching and application are discussed further, and it p roved that quaternary encoding, spectral angle and SID can be used to information processing effectively. 1 Introduction Hyperspectral
6、 Remote Sensing was one of the most important breakthroughs of Earth Observation System ( EOS) in 1990 s. It overcomes the limitations of conventional aerial and multispectral RS such as less band amount, wide band scope and rough spectral information expression, and can provide RS information with
7、narrow band width, more band amount and fine spectral information, also it can distinguish and identify ground objects from spectral space, so hyperspectral RS has got wide applications in resources, environment, city and ecological fields. Because hyperspectral RS is different from conventional RS
8、information obviously in both information acquisition and information processing, there are many problems should be solved in practice. One of the most important problems is about spectral features extraction and application in hyperspectral RS data including hyperspectral RS image and standard spec
9、tral database. Nowadays, studies on hyperspectral are mainly focused on band selection and dimensionality reduction, image classification, mixed pixel decomposition and others, and studies on spectral features are few. In this paper, spectral features extraction and application will be taken as our
10、central topic in order to provide some useful advices to hyperspectral RS applications. 2 Framework of spectral features in hyperspectral RS data In general, hyperspectral RS image can be expressed by a spatial-spectral data cube ( Fig. 1). In this data cube, every coverage expressed the image of on
11、e band, and each pixel forms a spectral vector composed of albedo of ground object on every band in spectral dimension, and that vector can be visualized by spectral curve ( Fig. 2 ). Many features can be extracted from spectral vector or curve, and spectral features are the key and basis of hypersp
12、ectral RS applications. Also each spectral curve in spectral 2 database can be analyzed with same method. Although there are some algorithms to compute spectral features, the framework and system is still not obvious, so we would like to propose a framework for spectral features in hyperspectral RS
13、data including hyperspectral RS image and standard spectral database. Fig. 1 Hyperspectral image data cube Fig. 2 Reflectance spectral curve of a pixel 2. 1 Three scales of spectral features According to the operational objects of extraction algorithms, spectral features can be categorized into thre
14、e scales: point-scale, block-scale and volume- Scale. Point scale takes pixel and its spectral curve as operational object and some useful features can be extracted from this spectral vector (or spectral curve).In general, hyperspectral RS image takes spectral vector of each pixel as processing obje
15、ct. Block scale is oriented image block or region. Block is the set of some pixels, and it can be homogeneous or heterogeneous. Homogeneous regions are got by image segmentation and pixels in this region are similar in some given features； heterogeneous region are those image blocks with regular or
16、irregular size, and they are cut from original image directly, for example, an image can be segmented according to quadtree method. In hyperspectral RS image, block scale features can be computed from two aspects. One is to compute texture feature of a block on some characterized bands, and the othe
17、r is to compute spectral feature of a block. If the block is homogeneous its mean vector can be computed firstly and then spectral of this mean vector can be extracted to describe the block. If the block is heterogeneous, it can be segmented to some homogeneous blocks. Volume scale combines spatial
18、and spectral features in a whole and extracts features in 3D ( row, column and spectra ) space. Here, some 3D operational algorithms are needed, for example, 3D wavelet transformation and high order Artificial Neural Network (ANN ). Because this type of features is difficult to compute and analyze, we dont research it in current studies. In this paper, we would like to focus on point scale feature, or those features extracted from spectral
