基于流形学习和多目标光谱的高光谱目标检测

A. Ziemann, J. Theiler, D. Messinger
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引用次数: 18

摘要

从卫星和机载平台收集的图像为远程分析场景内容提供了重要工具。特别是,在场景中远程检测特定材料的能力在防扩散和其他应用中至关重要。处理高光谱图像的传感器系统收集执行这些检测分析所需的高维光谱信息。然而,对于d维高光谱图像,其中d是光谱带的数目,数据通常固有地占据m维空间,且m≪d。在遥感界,这导致了最近对使用流形学习的兴趣,该学习旨在描述数据离散近似的嵌入式低维非线性流形的特征。本文的研究重点是图论和流形学习方法来检测目标,使用局部线性嵌入的自适应版本,该方法偏向于将目标像素与背景像素分离。这种方法结合了特定材料的多个目标特征,考虑了通常存在于感兴趣的固体材料中的光谱变异性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hyperspectral target detection using manifold learning and multiple target spectra
Imagery collected from satellites and airborne platforms provides an important tool for remotely analyzing the content of a scene. In particular, the ability to remotely detect a specific material within a scene is of critical importance in nonproliferation and other applications. The sensor systems that process hyperspectral images collect the high-dimensional spectral information necessary to perform these detection analyses. For a d-dimensional hyperspectral image, however, where d is the number of spectral bands, it is common for the data to inherently occupy an m-dimensional space with m ≪ d. In the remote sensing community, this has led to recent interest in the use of manifold learning, which seeks to characterize the embedded lower-dimensional, nonlinear manifold that the data discretely approximate. The research presented here focuses on a graph theory and manifold learning approach to target detection, using an adaptive version of locally linear embedding that is biased to separate target pixels from background pixels. This approach incorporates multiple target signatures for a particular material, accounting for the spectral variability that is often present within a solid material of interest.
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