首页> 外文会议>Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI pt.2 >Performance Comparison of Hyperspectral Target Detection Algorithms in Altitude Varying Scenes
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Performance Comparison of Hyperspectral Target Detection Algorithms in Altitude Varying Scenes

机译:高空变化场景中高光谱目标检测算法的性能比较

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Many different hyperspectral target detection algorithms have been developed and tested under various assumptions, methods, and data sets. This work examines the spectral angle mapper (SAM), adaptive coherence estimator (ACE), and constrained energy maximization (CEM) algorithms. Algorithm performance is examined over multiple images, targets, and backgrounds. Methods to examine algorithm performance are plentiful and several different metrics are used here. Quantitative metrics are used to make direct comparisons between algorithms. Further analysis using visual performance metrics is made to examine interesting trends in the data. Results show an increase in detection algorithm performance as image altitude increases and spatial information decreases. Theories to explain this phenomenon are introduced.
机译:在各种假设,方法和数据集下,已经开发并测试了许多不同的高光谱目标检测算法。这项工作检查了频谱角度映射器(SAM),自适应相干估计器(ACE)和约束能量最大化(CEM)算法。在多个图像,目标和背景上检查算法性能。检查算法性能的方法很多,这里使用了几种不同的指标。定量指标用于在算法之间进行直接比较。使用视觉性能指标进行了进一步分析,以检查数据中有趣的趋势。结果表明,随着图像高度的增加和空间信息的减少,检测算法的性能也随之提高。介绍了解释这种现象的理论。

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