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首页> 外文会议>Asian conference on remote sensing;ACRS >FEATURE EXTRACTION AND CLASSIFICATION OF HYPERSPECTRAL IMAGES
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FEATURE EXTRACTION AND CLASSIFICATION OF HYPERSPECTRAL IMAGES

机译：高光谱图像的特征提取与分类

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摘要

In recent years, the processing and analysis of hyperspectral images have become the main tasks of many researchers dealing with RS image processing. Unlike the traditional multispectral datasets taken in the optical range of electro-magnetic spectrum, the hyperspectral data deals with an enormous amount of bands and the data are formed as collections of hundreds of images of the same scene with each image corresponding to a narrow interval of the electro-magnetic wavelength. It is clear that such datasets offer the superior potential for more accurate and detailed information extraction than is possible with other types of RS data. In this research, Hyperspectral Imager (Hyperion) and Advanced Land Imager (ALI) sensor images have been used onboard EO-1 satellite. The goal of this paper is to compare two different approaches in geological feature extraction for an image classification. Before the classification spectral and spatial enhancements are applied. Advanced satellite images classification represents an accurate and cost effective for land cover mapping at regional scale.The output of each of the feature extraction method is classified using a maximum likelihood classification and spectral angle mapper methods. The results are analyzed and compared.
机译：近年来，高光谱图像的处理和分析已成为许多研究RS图像处理的研究人员的主要任务。与在电磁光谱的光学范围内获取的传统多光谱数据集不同，高光谱数据处理大量的波段，并且数据形成为同一场景的数百个图像的集合，每个图像对应于一个狭窄的间隔。电磁波长。显然，与其他类型的RS数据相比，此类数据集为更准确，详细的信息提取提供了巨大的潜力。在这项研究中，高光谱成像仪（Hyperion）和高级陆地成像仪（ALI）传感器图像已被用于EO-1卫星。本文的目的是比较地质特征提取中用于图像分类的两种不同方法。在分类之前，应先应用频谱和空间增强功能。先进的卫星图像分类代表了在区域范围内进行土地覆盖制图的准确和经济有效的方法。每种特征提取方法的输出均使用最大似然分类和光谱角度映射器方法进行分类。分析结果并进行比较。

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