An introduction of hyperspectral images for remote sensing.
高光谱遥测影像介绍。
Data compression is a key problem in the applications of hyperspectral images.
数据压缩是高光谱图像处理应用中的一个关键问题。
In fact, however, some researches don't need all information of hyperspectral images.
事实上，有很多高光谱的应用研究并不需要用到高光谱图像的全部信息。
Classification is an important means for analysis and application of hyperspectral images.
图像分类是高光谱遥感图像分析与应用的重要手段。
The technical principle and methods of hyperspectral images pre-processing were described.
详细描述了对超光谱图像进行预处理的技术原理和方法。
Used in aerial photography, microscopy and any other applications requiring hyperspectral images.
用于航空摄影，显微镜摄影及其它需要超光谱仪成像之处。
Experimental results show that the method can effectively and reliably detect the target from hyperspectral images.
结果表明，该方法能够快速、可靠的检测出小目标。
However, because of the affection of various factors, hyperspectral images are usually seriously corrupted by stripe noises.
但由于各种因素影响，高光谱图像受到了严重的条带噪声干扰。
And it explore directly the spectral correlations of hyperspectral images. Its method is very simple and easy to be implemented.
预测技术是最简单的一种方法，它直接探索谱带与谱带之间的相关性，具有算法简单、易于实现的特点。
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。
In order to improve the classification accuracy of hyperspectral images, a fuzzy maximum likelihood classification method is proposed.
为了提高超谱图像分类的精度，提出了模糊最大似然分类算法。
A fractal feature may be analyzed spectrally and spatially due to the "combination of spectrum and image" character inhered in hyperspectral images.
高光谱影像具有“谱像合一”的特征，因而可以从谱和像两个角度对高光谱影像进行分形分析。
The experimental results show that the Projection Pursuit based on dynamical evolutionary approach is an effective means to detect anomaly target in hyperspectral images.
实验结果表明了基于动力演化算法的投影寻踪在高光谱影像异常目标检测中的有效性。
Hyperspectral images can provide much more information than multispectral images do and can solve many problems which can not be solved by multispectral imaging technology.
高光谱图像可以获得比多光谱图像更丰富的信息，并使得许多原先用多光谱信息不能解决的问题现在可以得到解决。
The test results show that the 3D SPIHT algorithm based on 3D DWT for hyperspectral images compression is very efficient, the algorithm is embedded and has modest complexity.
实验证明，基于3维小波变换的3维SPIHT编码算法在对超光谱图像压缩时，表现出了优良的率失真性能。 并且算法复杂度适中，具有嵌入式特性。
However, classifying hyperspectral images only with traditional classification algorithms will result in low classification precision, data redundancy and great waste of resource.
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。
Optical remote sensing relies on the use of panchromatic, multispectral and hyperspectral images with a high spectral resolution and spatial resolution to distinguish the observed scene.
光学遥感依赖于利用多光谱和超光谱图像具有较高的光谱分辨率和空间分辨率来对所观测场景进行辨别。
Hyperspectral images are widely concerned due to their high spectral resolution. Their application is a task of top priority after solving the problems of radiance correction and calibration.
超谱遥感图像由于其高光谱分辨率的特点正受到国外国内的广泛关注。
The article used absolute exponent as one of the methods of fuzzy diagnosis to identify and classify hyperspectral images, then evaluated precision of classification result based on pixel level.
文中采用模糊识别的绝对值指数法对高光谱图像进行识别分类，并对分类结果进行像元级的评价。
Hyperspectral remote sensing is an art, which integrates the spectrum representing to the radiant attributes of ground object with the homological images standing for spatial and geometric relations.
高光谱遥感是一门将反映地物辐射属性的光谱与反映地物空间和几何关系的图像结合在一起的技术。
Hyperspectral remote sensing images contain abundant spectral information for accurate change detection.
高光谱遥感影像包含了丰富的光谱特征，为精确的变化检测提供了依据。
The SOFM algorithm based on a neural network and an improved algorithm have a good effect on space compression of hyperspectral remote sensing images.
基于神经网络的SOFM算法及其改进算法取得较好的空间压缩效果，实现了对高光谱遥感图像的有效压缩。
We particularly analyze the character of the Large Aperture Static Imaging Spectrometer (LASIS) hyperspectral interference images and give the inclusions.
论文详细分析了大孔径静态干涉成像光谱仪(Large Aperture StaticImagingSpectrometer, LASIS)高光谱干涉图像的特点，并得出了相关的结论。
Using fuzzy clustering, the original bands set of hyperspectral remotely sensed images is divided into some fuzzy equivalent subset.
通过模糊聚类，得到对高光谱遥感影像原始波段集合的模糊等价划分。
Using fuzzy clustering, the original bands set of hyperspectral remotely sensed images is divided into some fuzzy equivalent subset.
通过模糊聚类，得到对高光谱遥感影像原始波段集合的模糊等价划分。
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