Taxonomy of detection algorithms for hyperspectral imaging applications

Dimitris Manolakis

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Taxonomy of detection algorithms for hyperspectral imaging applications

机译：高光谱成像应用检测算法的分类

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摘要

A unified, simplified, and concise overview of spectral target detection algorithms for hyperspectral imaging applications is presented. We focus on detection algorithms derived using established statistical techniques and whose performance is predictable under reasonable assumptions about hyperspectral imaging data. The emphasis on a signal processing perspective enables us to better understand the strengths and limitations of each algorithm, avoid unrealistic performance expectations, and apply an algorithm properly and sensibly.

机译：本文对高光谱成像应用中的光谱目标检测算法进行了统一、简化和简明的概述。我们专注于使用成熟的统计技术得出的检测算法，其性能在高光谱成像数据的合理假设下是可预测的。对信号处理视角的强调使我们能够更好地了解每种算法的优势和局限性，避免不切实际的性能预期，并正确、明智地应用算法。

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来源
《Optical Engineering》 |2005年第6期|066403-1-066403-11-0|共11页
作者
Dimitris Manolakis;
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作者单位

MIT Lincoln Laboratory 244 Wood Street Lexington, Massachusetts 02420-9185;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
中图分类光学仪器;
关键词
target detection; hyperspectral imaging; spectral matching algorithms;

机译：目标检测;高光谱成像;光谱匹配算法;

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Taxonomy of detection algorithms for hyperspectral imaging applications

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