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首页> 外文会议>Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII pt.1 >Discriminant Analysis with Nonparametric Estimates for Subpixel Detection of 3D Objects in Hyperspectral Imagery
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Discriminant Analysis with Nonparametric Estimates for Subpixel Detection of 3D Objects in Hyperspectral Imagery

机译：高光谱影像中3D物体亚像素检测的非参数估计判别分析

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摘要

The large amount of spectral information in hyperspectral imagery allows the accurate detection of subpixel objects. The use of subspace models for targets and backgrounds allows detection that is invariant to changing environmental conditions. The non-Gaussian behavior of target and background distribution residuals complicates the development of sub space-based detection methods. In this paper, we use discriminant analysis for feature extraction for separating subpixel 3D objects from cluttered backgrounds. The nonparametric estimation of distributions is used to establish the statistical models using the length and direction of residuals. Candidate subspaces are then evaluated to maximize their discriminatory power which is measured between estimated distributions of targets and backgrounds. In this context, a likelihood ratio test is used based on background and mixed statistics for subpixel detection. The detection algorithm is evaluated for HYDICE images and a number of images simulated using DIRSIG under a variety of conditions. The experimental results demonstrate accurate detection performance on these data sets.
机译：高光谱图像中的大量光谱信息可以精确检测亚像素对象。对目标和背景使用子空间模型可以进行不断变化的环境条件检测。目标和背景分布残差的非高斯行为使基于子空间的检测方法的开发复杂化。在本文中，我们使用判别分析进行特征提取，以从混乱的背景中分离出亚像素3D对象。分布的非参数估计用于使用残差的长度和方向建立统计模型。然后评估候选子空间以最大化其区分能力，该能力在目标和背景的估计分布之间进行测量。在这种情况下，基于背景和混合统计量的似然比测试用于子像素检测。在各种条件下，针对HYDICE图像和使用DIRSIG模拟的许多图像对检测算法进行评估。实验结果证明了在这些数据集上的准确检测性能。

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