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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >On the classification of classes with nearly equal spectral response in remote sensing hyperspectral image data
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On the classification of classes with nearly equal spectral response in remote sensing hyperspectral image data

机译：遥感高光谱图像数据中光谱响应几乎相等的类的分类

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摘要

It is well known that high-dimensional image data allows for the separation of classes that are spectrally very similar, i.e., possess nearly equal first-order statistics, provided that their second-order statistics differ significantly. The aim of this study is to contribute to a better understanding, from a more geometrically oriented point of view, of the role played by the second-order statistics in remote sensing digital image classification of natural scenes when the classes of interest are spectrally very similar and high dimensional multispectral image data is available. A number of the investigations that have been developed in this area deal with the fact that as the data dimensionality increases, so does the difficulty in obtaining a reasonably accurate estimate of the within-class covariance matrices from the number of available labeled samples, which is usually limited. Several approaches have been proposed to deal with this problem. This study aims toward a complementary goal. Assuming that reasonably accurate estimates for the within-class covariance matrices have been obtained, we seek to better understand what kind of geometrically-oriented interpretation can be given to them as the data dimensionality increases and also to understand how this knowledge can help the design of a classifier. In order to achieve this goal, the covariance matrix is decomposed into a number of parameters that are then analyzed separately with respect to their ability to separate the classes. Methods for image classification based on these parameters are investigated. Results of tests using data provided by the sensor system AVIRIS are presented and discussed.
机译：众所周知，高维图像数据允许分离光谱上非常相似的类，即，具有几乎相等的一阶统计量，前提是它们的二阶统计量显着不同。这项研究的目的是，从更注重几何的角度出发，有助于更好地理解当感兴趣的类别在光谱上非常相似时，二阶统计量在自然场景的遥感数字图像分类中所起的作用。高维多光谱图像数据可用。在这一领域进行的许多研究都涉及这样一个事实，即随着数据维数的增加，从可用标记样本的数量中获得类内协方差矩阵的合理准确估计的困难也越来越大。通常有限。已经提出了几种方法来解决这个问题。这项研究旨在实现一个补充目标。假设已经获得了对类内协方差矩阵的合理准确的估计，我们试图更好地理解随着数据维数的增加可以对它们进行哪种几何定向的解释，并了解这种知识如何帮助设计分类器。为了实现此目标，将协方差矩阵分解为多个参数，然后就其分离类的能力进行单独分析。研究了基于这些参数的图像分类方法。介绍并讨论了使用传感器系统AVIRIS提供的数据的测试结果。

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