高光谱异常变化检测算法分析

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) Pub Date : 2016-08-01 DOI:10.1109/WHISPERS.2016.8071746

Yair Elhadad, S. Rotman, D. Blumberg

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引用次数: 1

摘要

本文对高光谱图像中的异常变化检测算法进行了测试。专注于基于差分的算法，我们的目标是使用利用图像的空间和统计特征的新方法来优化性能。这些方法增加了检测的概率，同时最大限度地减少了误报。算法在罗彻斯特理工学院(RIT)的高光谱图像上进行了测试。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Analysis of hyperspectral anomaly change detection algorithms

In this paper, we test anomaly change detection algorithms in hyperspectral images. Focusing on difference-based algorithms, our goal is to optimize performance using new methods that utilize the spatial and statistical characteristics of the images. These methods increase the probability of detection while minimizing false alarms. The algorithms are tested on the hyperspectral images of the Rochester Institute of Technology (RIT).

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2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)

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