对降维后的高光谱数据采用SVM进行分类。
The dimension reduction of hyperspectral data was classified by SVM.
使用这个高光谱数据，科学家已经为冰面硫磺沉积物的位置绘制了地图(红色轮廓)。
Using this hyperspectral data, scientists have mapped the location of sulfur deposits on the ice surface (red outline).
使用这个高光谱数据，科学家已经为冰面硫磺沉积物的位置绘制了地图(红色轮廓)。
Using this hyperspectral data, scientists have mapped the location of sulfur deposits on the ice surface (green outline).
根据水稻生长期的高光谱数据的光谱特征，设计了一个混合决策树分类算法。
According to the rice spectral features of hyperspectral image data acquired during the rice is growing, a hybrid decision tree classification algorithm dealing with the variety of rice is developed.
一种高光谱遥感数据多类别监督分类方法，包含以下步骤：（1）读入 高光谱数据；
The invention relates to a supervised classification method of multi-class hyperspectrum remotely sensed data, which comprises the following steps: (1), reading the hyperspectrum data;
本文在深入分析高光谱数据特点的基础上，系统地研究了基于光谱维的图像异常检测方法。
Based on the analysis of characteristics of hyperspectral imagery, the methods of anomaly detection are studied systematically in this paper.
使用高光谱数据描述一个类似于欧罗巴星的北极区富硫泉水地带，《环境遥感》，114（6），1297-1311.
Characterization of a sulfur-rich Arctic spring site and field analog to Europa using hyperspectral data. Remote Sensing of Environment, 114 (6), 1297-1311.
利用该方法进行处理，当高光谱数据维数降低了90%时，9类地物分类实验的总体分类精度可以达到80.2%。
When the data dimensionality is reduced 90% by using the proposed method, the overall classification accuracy of nine classes of ground cover reaches 80.2%.
通过获得的高光谱数据(hyperspectral data)，科学家绘制了冰层表面硫磺沉淀的范围(红色线)。
Hyperspectral data, scientists have mapped the location of sulfur deposits on the ice surface (red outline).
高光谱数据作为一种三维图像，不同于二维静止图像，也不同于视频图像，一般的图像压缩技术难以达到高光谱压缩的性能要求。
As a kind of three-dimensional data sets, it is different from the 2d still image, and is also different from video series, so the general image compression method is not efficient for them.
此外，高光谱和多光谱图像处理能力也得到扩展，可分析额外频段的图像数据，最大限度地减少对专用软件包的依赖。
Furthermore, hyperspectral and multispectral image processing is extended with the capacity to analyze additional bands of image data, which minimizes the dependency on specialized software packages.
数据压缩是高光谱图像处理应用中的一个关键问题。
Data compression is a key problem in the applications of hyperspectral images.
文章深入分析了高光谱遥感数据中噪声的特点，提出了一种基于平稳小波变换的改进小波滤噪算法。
This paper analyzed the characteristic of noise in hyperspectral data deeply, and puts forward a de-noising method based on stationary discrete wavelet transform (SDWT).
与常规遥感相比，高光谱遥感数据处理及目标地物的识别需要采用一些新的技术和手段。
Compared with traditional remote sensing, some new technique and method must be developed for hyperspectral remote sensing data processing and object discerning.
由于高光谱遥感数据具有非常高的光谱分辨率，因此非常有利于深入挖掘地物的理化特性或精细识别不同的地物。
For it has an extremely high spectral resolution, such data has facilities to the physicochemical characteristics mining or subtle recognition of different ground objects.
但是高光谱图像的每个像元都对应几十到几百个波段的灰度值，数据量极其庞大，因此必须对其进行压缩。
However, each pixel of hyperspectral image has tens or hundreds of magnitude values corresponding its wave bands, so the enormous data must be compressed.
高光谱图像压缩技术是遥感数据存储和传输中的一个迫切需要解决的问题。
The compression technology of hyperspectral image is a key problem to be solved in the storage and transportation of remote sensing data.
高光谱分辨率遥感数据以其丰富的光谱信息使得其分析处理集中于光谱维上进行图像信息的展开和定量分析。
We study on the image information and quantitative analysis focusing on the spectral dimension because the hyperspectral data has the abundant spectral information.
植被高光谱遥感以其显著的特点已经成为连接遥感数据处理、地面测量、光谱模型和应用的强有力的工具。
Hyperspectral remote sensing data, compared with wide band remote sensing data, has the advantage of high spectral resolution.
在统计意义上蚀变异常在遥感高(多)光谱数据集合中是可识别的。
In a statistical sense, the alteration anomaly of remote sensing hyper (multi -) spectral data set is identifiable.
并对非线性主折线算法用于高光谱遥感数据特征提取的效果进行研究和讨论。
A simplified algorithm of nonlinear principal curves called nonlinear principal poly line is developed and its effect for feature extraction of hyperspectral data is researched.
最近，高光谱图像已生效的图片，它提供了一个非常高的空间分辨率，同时拍摄的极其精细的辐射分辨率数据。
Recently, hyperspectral imagery has come into the picture, which provides a very high spatial resolution while capturing extremely fine radiometric resolution data.
高维遥感数据的分类与识别与传统的多光谱遥感分类技术具有明显的区别。
Classification and pattern recognition of high dimensional remote sensing data are distinctly different from traditional multi-channel remote sensing classification techniques.
针对高光谱遥感影像数据量大、数据冗余度高的特点，引入拉普拉斯特征映射方法对高光谱遥感数据进行非线性降维。
Feature extraction is an indispensable preprocessing step for large and high redundancy data of hyperspectral remote sensing image.
算法采用核函数变换的方式，将重叠严重和非线性的光谱数据进行高维空间变换后再计算各组分气体浓度。
The transformation of kernel function is used to solve the overlapped mixed gas feature absorption spectrum in high dimension space.
主要介绍PHI - 3高光谱遥感数据从原始数据到产品这一阶段的预处理情况。
The processing of PHI-3 hyperspectral remote sensing data from original data to production is presented.
但仅用传统分类算法对高光谱图像分类，会导致分类精度降低、空间数据冗余和资源的极大浪费。
However, classifying hyperspectral images only with traditional classification algorithms will result in low classification precision, data redundancy and great waste of resource.
随着数据量的不断增长，如何有效压缩高光谱图像成为影响其普及应用的一个关键问题。
Hyperspectral images are massive data consisting of hundreds of spectral bands and have been used in a large number of applications.
随着数据量的不断增长，如何有效压缩高光谱图像成为影响其普及应用的一个关键问题。
Hyperspectral images are massive data consisting of hundreds of spectral bands and have been used in a large number of applications.
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