首页> 外文会议>Algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery XIX >Spectral target detection using a physical model and a manifold learning technique
【24h】

Spectral target detection using a physical model and a manifold learning technique

机译:使用物理模型和流形学习技术进行光谱目标检测

获取原文
获取原文并翻译 | 示例

摘要

Identification of materials from calibrated radiance data collected by an airborne imaging spectrometer depends strongly on the atmospheric and illumination conditions at the time of collection. This paper presents a methodology for identifying material spectra using the assumption that each unique material class forms a lower-dimensional manifold (surface) in the higher-dimensional spectral radiance space and that all image spectra reside on, or near, these theoretic manifolds. Using a physical model, a manifold characteristic of the target material exposed to varying illumination and atmospheric conditions is formed. A graph-based model is then applied to the radiance data to capture the intricate structure of each material manifold followed by the application of the commute time distance (CTD) transformation to separate the target manifold from the background. Detection algorithms are than applied in the CTD subspace. This nonlinear transformation is based on a Markov-chain model of a random walk on a graph and is derived from an eigendecomposition of the pseudoinverse of the graph Laplacian matrix. This paper discusses the properties of the CTD transformation, the atmospheric and illumination parameters varied in the physics-based model and demonstrates the influence the target manifold samples have on the orientation of the coordinate axes in the transformed space. A comparison between detection performance in the CTD subspace and spectral radiance space is also given for two hyperspectral images.
机译:从机载成像光谱仪收集的校准辐射率数据中识别材料在很大程度上取决于收集时的大气和光照条件。本文提出了一种用于识别材料光谱的方法,该方法假设每个唯一的材料类别在高维光谱辐射空间中形成一个低维流形(表面),并且所有图像光谱都位于这些理论流形上或附近。使用物理模型,形成暴露于变化的光照和大气条件下的目标材料的多方面特性。然后将基于图形的模型应用于辐射数据,以捕获每个物料歧管的复杂结构,然后应用通勤时间距离(CTD)变换将目标歧管与背景分离。然后,将检测算法应用于CTD子空间。该非线性变换基于图上随机游动的马尔可夫链模型,并且是根据图拉普拉斯矩阵的伪逆的特征分解得出的。本文讨论了CTD变换的特性,基于物理模型的大气和光照参数的变化,并演示了目标流形样本对变换空间中坐标轴方向的影响。还针对两个高光谱图像在CTD子空间和光谱辐射度空间中的检测性能进行了比较。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号